Detailed knowledge of the intra‐field variability of soil properties and crop characteristics is indispensable for the establishment of sustainable precision agriculture. We present an approach that combines ground‐based agrogeophysical soil and aerial crop data to delineate field‐specific management zones that we interpret with soil attribute measurements of texture, bulk density, and soil moisture, as well as yield and nitrate residue in the soil after potato ( Solanum tuberosum L.) cultivation. To delineate the management zones, we use aerial drone‐based normalized difference vegetation index (NDVI), spatial electromagnetic induction (EMI) soil scanning, and the EMI–NDVI data combination as input in a machine learning clustering technique. We tested this approach in three successive years on six agricultural fields (two per year). The field‐scale EMI data included spatial soil information of the upper 0–50 cm, to approximately match the soil depth sampled for attribute measurements. The NDVI measurements over the growing season provide information on crop development. The management zones delineated from EMI data outperformed the management zones derived from NDVI in terms of spatial coherence and showed differences in properties relevant for agricultural management: texture, soil moisture deficit, yield, and nitrate residue. The combined EMI–NDVI analysis provided no extra benefit. This underpins the importance of including spatially distributed soil information in crop data interpretation, while emphasizing that high‐resolution soil information is essential for variable rate applications and agronomic modeling.

Yield and quality estimations provide vital information to fruit growers, yet require accurate monitoring throughout the growing season. To this end, the temporal dependency of fruit yield and quality estimations through spectral vegetation indices was investigated in irrigated and rainfed pear orchards. Both orchards were monitored throughout three consecutive growing seasons, including spectral measurements (i.e., hyperspectral canopy reflectance measurements) as well as yield determination (i.e., total yield and number of fruits per tree) and quality assessment (i.e., fruit firmness, total soluble solids and fruit color). The results illustrated a clear association between spectral vegetation indices and both fruit yield and fruit quality (|r| > 0.75; p < 0.001). However, the correlations between vegetation indices and production variables varied throughout the growing season, depending on the phenological stage of fruit development. In the irrigated orchard, index values showed a strong association with production variables near time of harvest (|r| > 0.6; p < 0.001), while in the rainfed orchard, index values acquired during vegetative growth periods presented stronger correlations with fruit parameters (|r| > 0.6; p < 0.001). The improved planning of remote sensing missions during (rainfed orchards) and after (irrigated orchards) vegetative growth periods could enable growers to more accurately predict production outcomes and improve the production process.

This study assessed farmers’ soil and irrigation water management practices, perceptions, and major constraints at Koga, a large-scale irrigation scheme in Ethiopia. Key informant interviews, structured and semi-structured questionnaires, focus group discussions, and field visits were used for data collection. Soil samples were collected for the assessment of soil properties and a comparison with the respondents’ perception of soil-related constraints. A total of 385 respondents were involved in the questionnaire. All of the respondents had a good perception of soil acidity and its management strategies. Respondents’ perception was in line with the mean soil pH, soil texture, infiltration rate, exchangeable acidity, and soil organic carbon obtained from lab analysis and field tests. Soil acidity, unwise use of water, water scarcity, and lack of market linkages hampered the performance of the Koga irrigation scheme. Yet, respondents had a low awareness of irrigation water management. Farmers never used irrigation scheduling, but apply the same amount of water regardless of the crop type. As a result, low yield and water use efficiency were reported. To reduce soil acidity, an adequate lime supply for farmers with hands-on training on how to apply it would be desirable. Farmers should be aware of how to design effective irrigation scheduling and adopt water-saving management strategies.