Rainfall defines livelihood patterns among agrarian communities of the climate-change vulnerable semi-arid Sub-Saharan Africa. However, it remains inadequately studied, resulting in ineffective water management policies and weak agricultural growth in the region. Monthly rainfall data collected between 1987 and 2018 at four stations along a 1200 km climatic gradient in northern Namibia were analysed for annual, seasonal and monthly trends and variability. Descriptive measures and the Mann-Kendall test were used for rainfall characterisation and trend detection, respectively. Results showed an annually increasing rainfall trend, but with a downward trend in the dry season decreasing by -0.14 mm year–1 and an upward trend in the rainy season increasing by 7.74 mm year–1 across the study area. The rainy-season mean monthly rainfall showed predominantly increasing trends, while the dry-season ones exhibited insignificant decreasing trends. The study detected a decreasing rainfall gradient from the northeast towards the northwest with a range of 156.8 mm and concomitant increasing spatial-temporal variability. The upward rainfall trend has implications for rainy season floods, whereas the downward trend suggests dry season drought intensification in the area. These results could be useful for rainwater management planning in the study area and other dryland regions.

The production of pearl millet (Pennisetum glaucum (L.) R.Br.) is important in Namibia, in sub-Saharan Africa, owing to the prevailing low precipitation conditions. Most fields supporting crop production in northern Namibia are located in a network of seasonal wetlands. The aim of the present study was to evaluate the effects of ridging and fertilizer application on the yield and the growth of pearl millet in the seasonal wetlands under different rainfall conditions. The study was conducted for two years (2017–2018) in the experimental fields in northern Namibia, and yield, yield components, and growth parameters were evaluated in relation to the application of different fertilizers (manure and mineral) with and without ridge-furrows. Manure fertilizer application presented the highest yield in 2018, whereas mineral fertilizer application showed the highest yield in 2017. The proportion of rainfall was the highest during the mid-growth period in 2017, and the reproductive stage in 2018. Thus, pearl millet plants under manure fertilization overcame damage resulting from waterlogging stress during the seed setting stage by improving the soil and plant nutrient conditions. In contrast, the plants under mineral fertilization were more tolerant to large amounts of rain during the mid-growth period. In this study, yield was mainly determined by total dry weight, and it was closely related to panicle density in both years. Therefore, we concluded that fertilizer application, including additional fertilizer based on the growth diagnostic, could be important for improving crop production in seasonal wetlands.

Bambara groundnut (Vigna subterranea (L) Verdc.) is an orphan, underutilised and less exploited crop in Africa and beyond, yet it is an essential traditional crop for subsistence farmers in sub-Saharan Africa. Currently, in Namibia, neither a pre-breeding nor a breeding programme exists for Bambara groundnut. Twenty-five Bambara groundnut accessions acquired locally and outside Namibia were characterised for descriptor state, including possible character diversity on the quantitative descriptors. Square lattice design with three replications was used. Data were analysed using analysis of variance (ANOVA), Pearson correlation moment, Principal Component Analysis (PCA), and cluster analysis. ANOVA indicated significant differences (P < 0.05) among accessions for most of the characters measured, and highly significant differences (P < 0.01) for the number of pods per plant, pod yield, seed yield, plant height, and dry biomass. The dendrogram sub-criterion indicated three clusters, confirming the results of the PCA, which grouped accessions with common descriptors in the same quadrants. PCA biplot showed that the first two components explained 59.55% of the variation. Overall results suggest that the Bambara groundnut accessions evaluated in this study showed high variability, thus can be used as a source of pre-breeding materials to initiate a national breeding programme.

In semi-arid regions, climate change has affected crop growing season length and sowing time, potentially causing low yield of the rainfed staple crop pearl millet (Pennisetum glaucum L.) and food insecurity among smallholder farmers. In this study, we used 1994–2023 rainfall data from Namibia’s semi-arid North-Central Region (NCR), receiving November–April summer rainfall, to analyze rainfall patterns and trends and their implications on the growing season to propose climate adaptation options for the region. The results revealed high annual and monthly rainfall variabilities, with nonsignificant negative trends for November–February rainfalls, implying a shortening growing season. Furthermore, we determined the effects of sowing date on grain yields of the early-maturing Okashana-2 and local landrace Kantana pearl millet varieties and the optimal sowing window for the region, using data from a two-year split-plot field experiment conducted at the University of Namibia—Ogongo Campus, NCR, during the rainy season. Cubic polynomial regression models were applied to grain-yield data sets to predict grain production for any sowing date between January and March. Both varieties produced the highest grain yields under January sowings, with Kantana exhibiting a higher yield potential than Okashana-2. Kantana, sown by 14 January, had a yield advantage of up to 36% over Okashana-2, but its yield gradually reduced with delays in sowing. Okashana-2 exhibited higher yield stability across January sowings, surpassing Kantana’s yields by up to 9.4% following the 14 January sowing. We determined the pearl millet optimal sowing window for the NCR to be from 1–7 and 1–21 January for Kantana and Okashana-2, respectively. These results suggest that co-cultivation of early and late pearl millet varieties and growing early-maturing varieties under delayed seasons could stabilize grain production in northern Namibia and enhance farmers’ climate adaptation. Policymakers for semi-arid agricultural regions could utilize this information to adjust local seed systems and extension strategies.

Accurate and timely estimation of maize yield is crucial for ensuring food security, optimizing resource utilization, and informing agricultural policy. However, current yield estimation methods often encounter significant limitations, such as low spatial resolution, dependence on sparse ground-truth data, poor model generalizability across diverse agroecological zones, and challenges in integrating heterogeneous data sources. Although numerous techniques have been developed, ranging from traditional field-based measurements to advanced remote sensing and machine learning methods, a comprehensive synthesis that critically evaluates these approaches and explores their convergence is still lacking. This review addresses this gap by providing a systematic overview of recent advances in maize yield estimation, with a focus on remote sensing technologies, machine learning algorithms, and hybrid crop modeling frameworks. It examines the strengths and limitations of various methodologies, including UAV-and satellite-based imaging, hyperspectral and LiDAR sensing, regression and ensemble learning, and long-read sequencing. Additionally, the review explores the role of emerging technologies such as IoT, cloud computing, and blockchain in enhancing data collection, processing, and traceability. By identifying key challenges such as environmental variability, data scarcity, and model interpretability, and highlighting opportunities for methodological integration, this review offers a roadmap for future research and development. It argues that the convergence of digital agriculture tools and robust modeling strategies holds significant promise for improving maize yield estimation accuracy, scalability, and applicability. These advancements have far-reaching implications for sustainable agriculture, climate resilience, and global food security.