Optimizing water and nitrogen application for neglected horticultural species in tropical sub-humid climate areas: A case of African eggplant (Solanum aethiopicum L.)

African eggplant, a traditional and important nutrient-dense crop to Tanzania’s nutrition and food security. However, yields remain low as a result of sub-optimal irrigation and fertilizer practices. To reduce the yield gap, a randomized split-plot design set up with irrigation as a main and nitrogen (N) treatments as a sub-factor. The irrigation regimes were 100 % (I100), 80 % (I80) and 60 % (I60) of crop water requirements whilst nitrogen levels were 250 kg N/ha (F100), 187 kg N/ha (F75), 125 kg N/ha (F50) and 0 kgN/ha (F0). The study evaluated the effect of irrigation water and N on crop growth variables and yield, fruit quality, WUE and NUE. The study showed the importance of combining different irrigation performance indicators which responds to different levels of water and nitrogen to evaluate and assess suitable irrigation and fertilizer strategies for African eggplant. The crop growth variables (plant height and LAI) had a good correlation with fruit yield (R2 = 0.6 and 0.8). The fruit quality was best performed by 100 % water in combination with 75 % N treatment. The best WUE and NUE was attained at 80 % and 100 % levels of water in combination with 75 % N. However, minimizing trade-offs between the various indicators, the optimal application for African eggplant would likely be around 80 % of the total irrigation requirement and 75 % of the N requirement in sandy clay loam soils under tropical sub-humid conditions

Critical analysis of the electricity market in developing country municipality

DATA AVAILABILITY : Data will be made available on request.Proceedings of the 7th International Conference on Advances on Clean Energy Research, ICACER 2022, April 20–22, 2022, Barcelona, Spain.Developing countries are experiencing significant urban and population growth. This amplifies the crisis of governance of essential public services. This study analyses the electricity market in Lubumbashi, one of the Democratic Republic of Congo (DRC) municipalities. Surveys at different scales are conducted, assessing both access to electricity and the role of actors. 5270 households, 41 policymakers and 100 employees at different scales are interviewed. It has been found that the electricity market is deteriorated by illegal connections to the electricity grid, uncontrolled urban growth, corruption, and poverty. Besides, the quality and cost of electricity decrease with distance from the city centre. Therefore, privatization of the electricity sector would lead to low-cost electricity and high-quality service.http://www.elsevier.com/locate/egyrhj2023Electrical, Electronic and Computer Engineerin

Stakeholder-driven transformative adaptation is needed for climate-smart nutrition security in sub-Saharan Africa - author correction

oai:repository.rothamsted.ac.uk:99048Improving nutrition security in sub-Saharan Africa under increasing climate risks and population growth requires a strong and contextualized evidence base. Yet, to date, few studies have assessed climate-smart agriculture and nutrition security simultaneously. Here we use an integrated assessment framework (iFEED) to explore stakeholder-driven scenarios of food system transformation towards climate-smart nutrition security in Malawi, South Africa, Tanzania and Zambia. iFEED translates climate–food–emissions modelling into policy-relevant information using model output implication statements. Results show that diversifying agricultural production towards more micronutrient-rich foods is necessary to achieve an adequate population-level nutrient supply by mid-century. Agricultural areas must expand unless unprecedented rapid yield improvements are achieved. While these transformations are challenging to accomplish and often associated with increased greenhouse gas emissions, the alternative for a nutrition-secure future is to rely increasingly on imports, which would outsource emissions and be economically and politically challenging given the large import increases required

Stakeholder-driven transformative adaptation is needed for climate-smart nutrition security in sub-Saharan Africa

Improving nutrition security in sub-Saharan Africa under increasing climate risks and population growth requires a strong and contextualised evidence base. Yet, to date, few studies have assessed climate-smart agriculture and nutrition security simultaneously. Here we use an integrated assessment framework (iFEED) to explore stakeholder-driven scenarios of food system transformation towards climate-smart nutrition security in Malawi, South Africa, Tanzania and Zambia. iFEED translates climate-food-emissions modelling into policy-relevant information using model output implication statements. Results show that diversifying agricultural production towards more micronutrient-rich foods is necessary to achieve an adequate population-level nutrient supply by mid-century. Agricultural areas must expand unless unprecedented rapid yield improvements are achieved. Whilst these transformations are challenging to accomplish and often associated with increased greenhouse gas emissions, the alternative for a nutrition-secure future is to rely increasingly on imports, which would outsource emissions and be economically and politically challenging given the large import increases required

Addressing climate change with behavioral science: a global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors

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, Vol13(1): 12-25Agricultural 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

UAV-based multispectral vegetation indices for assessing the interactive effects of water and nitrogen in irrigated horticultural crops production under tropical sub-humid conditions: a case of African eggplant

UAV-based multispectral vegetation indices are often used to assess crop performance and water consumptive use. However, their ability to assess the interaction between water, especially deficit irrigation, and nitrogen application rates in irrigated agriculture has been less explored. Understanding the effect of water-nitrogen interactions on vegetation indices could further support optimal water and N management. Therefore, this study used a split plot design with water being the main factor and N being the sub-factor. African eggplants were drip irrigated at 100% (I100), 80% (I80) or 60% (I60) of the crop water requirements and received 100% (F100), 75% (F75), 50% (F50) or 0% (F0) of the crop N requirements. Results showed that the transformed difference vegetation index (TDVI) was best in distinguishing differences in leaf moisture content (LMC) during the vegetative stage irrespective of the N treatment. The green normalized difference vegetation index (GNDVI) worked well to distinguish leaf N during vegetative and full vegetative stages. However, the detection of the interactive effect of water and N on crop performance required a combination of GNDVI, NDVI and OSAVI across both stages as each of these 3 VI showed an ability to detect some but not all treatments. The fact that a certain amount of irrigation water can optimize the efficiency of N uptake by the plant is an important criterion to consider in developing crop specific VI based decision trees for crop performance assessments and yield prediction

The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L).

This study was conducted to evaluate the feasibility of a mobile phone-based thermal and UAV-based multispectral imaging to assess the irrigation performance of African eggplant. The study used a randomized block design (RBD) with sub-plots being irrigated at 100% (I100), 80% (I80) and 60% (I60) of the calculated crop water requirements using drip. The leaf moisture content was monitored at different soil moisture conditions at early, vegetative and full vegetative stages. The results showed that, the crop water stress index (CWSI) derived from the mobile phone-based thermal images is sensitive to leaf moisture content (LMC) in I80 and I60 at all vegetative stages. The UAV-derived Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI) correlated with LMC at the vegetative and full vegetative stages for all three irrigation treatments. In cases where eggplant is irrigated under normal conditions, the use of NDVI or OSAVI at full vegetative stages will be able to predict eggplant yields. In cases where, eggplant is grown under deficit irrigation, CWSI can be used at vegetative or full vegetative stages next to NDVI or OSAVI depending on available resources

Impact of projected climate change on agricultural production in semi-arid areas of Tanzania: A case of Same district

African Crop Science Journal 2012, Vol. 20, Issue Supplement s2, pp. 453 - 463Sub-Saharan Africa is one of the most vulnerable regions in the World to climate change because of widespread poverty and limited adaptive capacity. The future climate change is likely to present an additional challenge to the agricultural sector. Therefore, the effects of climate change on the current agronomic management practices were investigated using Same District, Tanzania as a case study area. APSIM software was used to investigate the response of maize (Zea mays L.) yield to different agronomic management practices using current and future (2046 - 2065) climate data. The climate change projections data from global climate models were downscaled using self-organising maps technique. Under the conventional practices, results show that during long rainy season (from March to May) there is yield decline of 13% for cultivar Situka, no change for cultivar Kito and increase of 10% and 15% for cultivars Sc401 and TMV1, respectively. Under the recommended practices, cultivars TMV1 and Sc401 are projected to register a 10% yield increase whereas cultivars Situka and Kito are projected to register a decrease of 10% and 45%, respectively. Also, under both conventional and recommended management practices, results showed that during short rainy season (from October to December/January) all cultivars are projected to register between 75% and 146% increase in maize yields. This implies that future climate change is going to have positive effects on current management practices during short rainy seasons and it will have negligible impact during long rainy seasons

Impact of projected climate change on agricultural production in semi-arid areas of Tanzania: A case of Same district

African Crop Science Journal 2012, Vol. 20, Issue Supplement s2, pp. 453 - 463Sub-Saharan Africa is one of the most vulnerable regions in the World to climate change because of widespread poverty and limited adaptive capacity. The future climate change is likely to present an additional challenge to the agricultural sector. Therefore, the effects of climate change on the current agronomic management practices were investigated using Same District, Tanzania as a case study area. APSIM software was used to investigate the response of maize (Zea mays L.) yield to different agronomic management practices using current and future (2046 - 2065) climate data. The climate change projections data from global climate models were downscaled using self-organising maps technique. Under the conventional practices, results show that during long rainy season (from March to May) there is yield decline of 13% for cultivar Situka, no change for cultivar Kito and increase of 10% and 15% for cultivars Sc401 and TMV1, respectively. Under the recommended practices, cultivars TMV1 and Sc401 are projected to register a 10% yield increase whereas cultivars Situka and Kito are projected to register a decrease of 10% and 45%, respectively. Also, under both conventional and recommended management practices, results showed that during short rainy season (from October to December/January) all cultivars are projected to register between 75% and 146% increase in maize yields. This implies that future climate change is going to have positive effects on current management practices during short rainy seasons and it will have negligible impact during long rainy seasons