Projected changes in East African climate and its impacts on climatic suitability of maize production areas by the mid-twenty-first century

The influence of climatic and environmental variables on sunflower planting season suitability in Tanzania

Crop survival and growth requires identification of correlations between appropriate suitable planting season and relevant climatic and environmental characteristics. Climatic and environmental conditions may cause water and heat stress at critical stages of crop development and thus affecting planting suitability. Consequently, this may affect crop yield and productivity. This study assesses the influence of climate and environmental variables on rain-fed sunflower planting season suitability in Tanzania. Data on rainfall, temperature, slope, elevation, soil and land use/or cover were accessed from publicly available sources using Google Earth Engine. This is a cloud-based geospatial computing platform for remote sensed datasets. Tanzania sunflower production calendar of 2022 was adopted to mark the start and end limits of planting across the country. The default climate and environmental parameters from FAO database were used. In addition, Pearson correlation was used to evaluate the relationship between rainfall, temperature over Normalized Difference Vegetation Index (NDVI) from 2000 to 2020 at five-year interval for January-April and June-September, for high and poor suitability season. The results showed that planting suitability of sunflower in Tanzania is driven more by rainfall than temperature. It was revealed that intra-annual planting suitability increases gradually from short to long- rain season and diminishes towards dry season of the year. January-April planting season window showing highest suitability (41.65%), whereas June-September indicating lowest suitability (0.05%). Though, not statistically significant, rainfall and NDVI were positively correlated with r = 0.65 and 0.75 whereas negative correlation existed between temperature and NDVI with r = -- 0.6 and - 0.77. We recommend sunflower subsector interventions that consider appropriate intra-regional and seasonal diversity as an important adaptive mechanism to ensure high sunflower yields.

Time series and power law analysis of crop yield in some east African countries

We carry out a time series analysis on the yearly crop yield data in six east African countries (Burundi, Kenya, Somalia, Tanzania, Uganda and Rwanda) using the autoregressive integrated moving average (ARIMA) model. We describe the upper tail of the yearly crop yield data in those countries using the power law, lognormal, Fréchet and stretched exponential distributions. The forecast of the fitted ARIMA models suggests that the majority of the crops in different countries will experience neither an increase nor a decrease in yield from 2019 to 2028. A few exceptional cases correspond to significant increase in the yield of sorghum and coffee in Burundi and Rwanda, respectively, and significant decrease in the yield of beans in Burundi, Kenya and Rwanda. Based on Vuong's similarity test p-value, we find that the power law distribution captured the upper tails of yield distribution better than other distributions with just one exceptional case in Uganda, suggesting that these crops have the tendency for producing high yield. We find that only sugar cane in Somalia and sweet potato in Tanzania have the potential of producing extremely high yield. We describe the yield behaviour of these two crops as black swan, where the "rich getting richer" or the "preferential attachment" could be the underlying generating process. Other crops in Burundi, Kenya, Somalia, Tanzania, Uganda and Rwanda can only produce high but not extremely high yields. Various climate adaptation/smart strategies (use of short-duration pigeon pea varieties, use of cassava mosaic disease resistant cassava varieties, use of improved maize varieties, intensive manuring with a combination of green and poultry manure, early planting, etc) that could be adapted to increase yields in east Africa are suggested. The paper could be useful for future agricultural planning and rates calibration in crop risk insurance.

Aflatoxin contamination of maize and groundnut in Burundi: Distribution of contamination, identification of causal agents and potential biocontrol genotypes of Aspergillus flavus

Aflatoxin contamination of the staples maize and groundnut is a concern for health and economic impacts across sub-Saharan Africa. The current study (i) determined aflatoxin levels in maize and groundnut collected at harvest in Burundi, (ii) characterized populations of Aspergillus section Flavi associated with the two crops, and (iii) assessed aflatoxin-producing potentials among the recovered fungi. A total of 120 groundnut and 380 maize samples were collected at harvest from eight and 16 provinces, respectively. Most of the groundnut (93%) and maize (87%) contained aflatoxin below the European Union threshold, 4 μg/kg. Morphological characterization of the recovered Aspergillus section Flavi fungi revealed that the L-morphotype of A. flavus was the predominant species. Aflatoxin production potentials of the L-morphotype isolates were evaluated in maize fermentations. Some isolates produced over 137,000 μg/kg aflatoxin B1. Thus, despite the relatively low aflatoxin levels at harvest, the association of both crops with highly toxigenic fungi poses significant risk of post-harvest aflatoxin contamination and suggests measures to mitigate aflatoxin contamination in Burundi should be developed. Over 55% of the L-morphotype A. flavus did not produce aflatoxins. These atoxigenic L-morphotype fungi were characterized using molecular markers. Several atoxigenic genotypes were detected across the country and could be used as biocontrol agents. The results from the current study hold promise for developing aflatoxin management strategies centered on biocontrol for use in Burundi to reduce aflatoxin contamination throughout the value chain.

Knowledge of climate change and adaptation by smallholder farmers: evidence from southern Ethiopia

Climate change has the greatest negative impact on low-income countries, which burdens agricultural systems. Climate change and extreme weather events have caused Ethiopia's agricultural production to decline and exacerbated food insecurity over the last few decades. This study investigates whether farmers' awareness and perceptions of climate change play a role in climate change adaptation using climate-smart agricultural practices. To collect data, 385 households in Southern Ethiopia were sampled using a multistage sampling. A Heckman probit two-stage selection model was applied to investigate the factors influencing farmers' perceptions to climate change and adaptation measures through adoption of climate-smart agriculture practices, complemented with key informant interviews and focused group discussions. The results indicated that most farmers (81.80%) perceived that the local climate is changing, with 71.9% reporting increased temperature and 53.15% reporting decreasing rainfall distribution. Therefore, farmers attempted to apply some adaptation practices, including soil and water conservation with biological measures, improved crop varieties, agroforestry, improved breeds, cut and carry system, controlled grazing, and residue incorporation. The empirical results revealed that farmers adaptation to climate change through adoptions of CSA practices was significantly influenced by education, family size, gender, landholding size, farming experience, access to climate information, training received, social membership, livestock ownership, farm income and extension services. The study found that farmers' perceptions of climate change and variability were significantly influenced by their age, level of education, farming experience, and access to climate information, hence, the need to focus on enhancing the accuracy of weather information, strengthening extension services, and considering a gender-sensitive adaptation approach toward improving farmers' knowledge and aspirations. Agricultural policies should support the efforts of farmers to increase the reliance on climate risk and alleviate farmers' difficulties in adopting climate-smart agriculture practices.