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Mapping the within-field variability of crop status is of great importance in precision agriculture, which seeks to balance agronomic inputs with spatial crop demands. Satellite imagery and the delineation of management zones based on remote sensing plays a key role. However, satellite imagery is dependent on a cloud-free view, which is especially challenging in temperate regions such as Northern Europe. This disadvantage can be overcome with unmanned aerial vehicles (UAV), which provide an alternative to satellites. An investigation was conducted to establish whether UAV imagery can generate similar crop heterogeneity maps to satellites (Sentinel 2) and the extent to which crop heterogeneity and management zones can be reproduced by repeated data collection within short time intervals. Three winter wheat fields were monitored during the growing season. Two vegetation indices (NDVI and MSAVI2) based on red and near-infrared (NIR) reflectance were calculated to delineate fields into five management zones based on NDVI raster maps using quintiles. The Pearson correlation coefficient, the Nash–Sutcliffe agreement coefficient and the smallest real difference coefficient (SRD), also called the reproducibility coefficient were used to evaluate the reproducibility. NDVI and MSAVI2 gave similar results, but NDVI was a slightly better descriptor of crop heterogeneity after canopy closure and NDVI was used for the remainder of the study. The results showed that substitution of satellite data with UAV data resulted in an average reclassification of 10 m by 10 m management zones corresponding to 58% of the total field area. Reclassification means that management pixels were classified differently according to origin of images. Repeated satellite and UAV imagery resulted in 39% and 47% reclassification, respectively. The results showed that the reproduction of remote sensing data with different sensor systems added more measurement error to measurements than was the case with repeated measurements using the same sensor systems. In this study, SRD averaged 2.5 management zones, which means that differences up to 2.5 management zones were within the measurement error. This paper discusses the practical aspects of these findings and clarifies that the reclassification of management zones is depending on the heterogeneity of the studied fields. Therefore, the achieved results may not be generalized but the presented methodology can be used in future studies.

Variable rate application (VRA) is a crucial tool in precision agriculture, utilizing platforms such as Google Earth Engine (GEE) to access vast satellite image datasets and employ machine learning (ML) techniques for data processing. This research investigates the feasibility of implementing supervised ML models (random forest (RF), the support vector machine (SVM), gradient boosting trees (GBT), classification and regression trees (CART)) and unsupervised k-means clustering in GEE to generate accurate management zones (MZs). By leveraging Sentinel-2 satellite imagery and yielding monitor data, these models calculate vegetation indices to monitor crop health and reveal hidden patterns. The achieved classification accuracy values (0.67 to 0.99) highlight the potential of GEE and ML models for creating precise MZs, enabling subsequent VRA implementation. This leads to enhanced farm profitability, improved natural resource efficiency, and reduced environmental impact.

IINTRODUCTION : The rapid digitalization of agriculture in East Africa has spurred the adoption of precision fertilization tools, which optimize nutrient application and enhance crop yields. However, the extent of digital technology adoption, its benefits, and the challenges smallholder farmers face in the region remain unclear. METHODS : A systematic review adhering to PRISMA guidelines assessed the adoption of digital technologies for precision fertilization in East Africa. A comprehensive search of English-language studies published between 2010 and 2024 resulted in fifteen studies that met the inclusion criteria. RESULTS : The review highlights digital solutions that assist smallholder farmers in sustainable resource management, including mobile applications, ICT tools, Variable Rate Application (VRA), and AI/ML technologies. Reported benefits include improved crop productivity, increased economic efficiency, and enhanced environmental sustainability. However, issues with data accuracy, limited access to technology, affordability constraints, and low digital literacy hinder widespread adoption. DISCUSSION : The findings emphasize the need for further research and the development of tailored strategies to enhance digital agricultural practices in East Africa. Addressing socioeconomic and infrastructure challenges is crucial to ensuring equitable access and maximizing the effectiveness of digital precision fertilization tools. This review provides valuable insights to support stakeholders in developing sustainable, data-driven agricultural frameworks to improve regional food security.

In fields of undulating topography, where rainfed crops experience different degrees of water stress caused by spatial water variations, yields vary spatially within the same field, thus offering opportunities for variable rate application (VRA) of nitrogen fertilizer. This study assessed the spatial variations of yield gaps caused by lateral flows from high to low points, for rainfed wheat grown in Córdoba, Spain, over six consecutive seasons (2016–2021). The economic implications associated with multiple scenarios of VRA adoption were explored through a case study and recommendations were proposed. Both farm size (i.e., annual sown area) and topographic structure impacted the dynamics of investment returns. Under current policy-price conditions, VRA adoption would have an economic advantage in farms similar to that of the case study with an annual sown area greater than 567 ha year−1. Nevertheless, current trends in energy prices, transportation costs and impacts on both cereal prices and fertilizers costs enhance the viability of VRA adoption for a wider population of farm types. The profitability of adopting VRA improves under such scenarios and, in the absence of additional policy support, the minimum area for adoption of VRA decreases to a range of 68–177 ha year−1. The combination of price increases with the introduction of an additional subsidy on crop area could substantially lower the adoption threshold down to 46 ha year−1, making VRA technology economically viable for a much wider population of farmers.