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Salt-affected soils have serious implications for agricultural land quality and productivity, leading to a reduction in the net cultivable area available for food production. This issue has emerged as one of the foremost global challenges in recent years, impacting both food security and environmental sustainability. This research focuses on soil characterizations conducted in three irrigation schemes to understand the types, distribution, and nature of salt-affected soils. Auger observations served as the basic methodology for soil unit delineation in this study. Twelve representative soil pits with a depth of more than 160 cm were described, and undisturbed and disturbed samples were collected and analyzed for physical and chemical properties. The schemes were characterized by low levels of available phosphorus, organic carbon, and total nitrogen, as well as variations in exchangeable bases. Furthermore, 37.5% of the analyzed horizons highlighted the presence of salt-affected soils. Sodium was the predominant cation, followed by magnesium, potassium, and calcium. Likewise, HCO3− was the dominant anion, followed by Cl− and SO42−. The results of this study reveal the existence of two types of salt-affected soil. Uturo irrigation scheme had sodic soils, while the Ndungu irrigation scheme had saline-sodic soils. Additionally, both sodic and saline-sodic soils have been discovered in the Mawala irrigation scheme. The root cause of this challenge is inherent soil conditions, exacerbated by inadequate drainage infrastructure. Therefore, measures such as the application of gypsum followed by salt leaching, improving soil drainage by incorporating organic matter, improving drainage infrastructure, and using rice-tolerant varieties are recommended to mitigate salt stress and improve soil fertility. It is imperative to establish a robust framework for continuous monitoring and regular assessment of soil health to enable timely interventions and informed decisions for sustain

The resilience of rice production to floods is a crucial aspect of agricultural development in flood-prone regions. However, little is known about the farming practices and challenges of rice farmers in two river basins in Tanzania: Lower-Rufiji and Kilombero, where rice is the main crop grown. This study aimed to investigate the experiences of farmers in these two rice-growing floodplains regarding the impact of floods on lowland rice production and farmer livelihoods, and the strategies they use to cope with floods. The study used a mixed-methods approach that involved household surveys, key informant interviews, and transect walks to collect data from 180 rice farmers and 14 experts and community leaders. The data analysis revealed that floods were the dominant stressor affecting rice production, occurring mostly during the long rainy season and experienced by all farmers in both locations. Floods not only caused significant crop losses, especially in Lower-Rufiji where farmers reported losing between 75 and 100% of their yield in cases of extreme floods, but also made it difficult to establish and manage crops, leading to crop land abandonment. Floods also affected the livelihoods of rice farmers by forcing them to abandon, rent, or sell their farms. Coexistence of floods with other biotic and abiotic stresses such as crop diseases and droughts also limited the use of short maturing high yielding rice varieties. Farmers have adopted on-farm strategies such as adjusting planting schedules, using different varieties of rice, changing to other crops or activities after the floods to cope with floods, but these practices face numerous challenges. The study recommends the development of early warning systems, breeding for flood-tolerant rice varieties, introgression of tolerance traits to multiple stresses and improving crop establishment methods to enhance the resilience of farmers to floods and other extreme climate events.

In Tanzania, 71% of rice is grown in a rainfed lowland rice production ecosystem, primarily in river basins where extreme weather events like floods are frequent. For a six-year period (2017–2022), flood mapping was conducted using Sentinel-1 data in the Google Earth Engine (GEE) platform, utilizing change detection and thresholding methodology. In addition to flood mapping, land use and land cover (LULC) were also analyzed using Sentinel-2 data in GEE, employing the Random Forest (RF) algorithm for classification. The aim was to understand the spatiotemporal extent of floods in two study locations. The resulting flood maps achieved an overall accuracy (OA) greater than 90% for all sites and study years. The findings revealed that agricultural land was the predominant land use/cover in both sub-basins, and floods were widespread in both regions. The study highlighted the interannual variability in flood extent, both spatially and temporally. Specifically, at the Ikwiriri site, floods were more extensive in 2020, covering 54.95% of the cultivated area, while in 2017, the minimum flood extent occurred, affecting 14% of the cultivated area. Similarly, at the Mngeta site, extensive floods were observed in 2020, with floods impacting 5.53% of the cultivated areas, while lower flood extents were observed in 2017, affecting 1.49% of the cultivated areas. Furthermore, the study demonstrated distinct spatiotemporal patterns of floods in both locations, with areas in proximity to rivers and wetlands experiencing more frequent floods. The research showcased the capabilities of the GEE cloud computation platform for flood inundation mapping, emphasizing its potential for enhancing our understanding of rice-producing environments. The generated flood maps can be utilized to guide the selection of areas for trials of flood-tolerant rice varieties and the dissemination of technologies such as flood-tolerant rice varieties, contributing to the resilience of rice farmers in these tw