The production of olive oil in Portugal and other countries of the Mediterranean region has greatly increased in recent years. Intensification efforts have focused on the growth of the planted area, but also on the increase of the orchards density and the implementation of irrigation systems. Concerns about possible negative impacts of modern olive orchard production have arisen in the last years, questioning the trade-offs between the production benefits and the environmental costs. Therefore, it is of great importance to review the research progress made regarding agronomic options that preserve ecosystem services in high-density irrigated olive orchards. In this literature review, a keywords-based search of academic databases was performed using, as primary keywords, irrigated olive orchards, high density/intensive/hedgerow olive orchards/groves, irrigation strategies, and soil management. Aside from 42 general databases, disseminated research, and concept-framing publications, 112 specific studies were retrieved. The olive orchards were classified as either traditional (TD) (50–200 trees ha⁻¹), medium-density (MD) (201–400 trees ha⁻¹), high-density (HD) (401–1500 trees ha⁻¹), or super-high-density (SHD) orchards (1501–2500 trees ha⁻¹). For olive crops, the ETc ranged from 0.65 to 0.70, and could fall as low as 0.45 in the summer without a significant decrease in oil productivity. Several studies have reported that intermediate irrigation levels linked with the adoption of deficit irrigation strategies, like regulated deficit irrigation (RDI) or partial rootzone drying (PRD), can be effective options. With irrigation, it is possible to implement agroecosystems with cover crops, non-tillage, and recycling of pruning residues. These practices reduce the soil erosion and nutrient leaching and improve the soil organic carbon by 2 to 3 t C ha⁻¹ year⁻¹. In this situation, in general, the biodiversity of plants and animals also increases. We expect that this work will provide a reference for research works and resource planning focused on the improvement of the productive and environmental performance of dense irrigated olive orchards, thereby contributing to the overall enhancement of the sustainability of these expanding agroecosystems.

This study aimed to assess the implications of Zn enrichment in wheat grains as a function of contrasting genotypes, edaphic conditions and foliar fertilizers. Triticum aestivum L. varieties Roxo and Paiva were grown in four production fields, and sprayed with ZnSO₄ (0, 16.20 and 36.40 kg/ha) Zn-EDTA (0, 6.30 and 12.60 kg/ha) and Tecnifol Zinc (0, 3.90 and 7.80 kg/ha). The heterogeneous edaphic conditions of the wheat fields were chemically characterized, it being found that soil properties determine different Zn accumulation in the grains of both genotypes. Foliar spraying enhanced to different extents Zn content in the grains of both genotypes, but the average of enrichment indexes varied among the wheat fields. Zinc mostly accumulated in the embryo and vascular bundle and to a lesser extent in the endosperm. Grain yield and test weight sprayed by ZnSO₄ gave the highest values in both genotypes, but the opposite was found for Zn-EDTA. Considering the color parameters, lightness and red–green transitions were found to be a conjunction of genotype characteristics, fertilization types and edaphic conditions prevailing in each field. It is concluded that the index of Zn enrichment in wheat grains is a docket of edaphic conditions, genotype and type of fertilization.

The evaluation of the spatial and temporal variability of soil properties can be valuable to improve crop productivity and soil health. A study of soil properties was carried out in southern Portugal, in three farm fields with irrigated annual crops (layers 0–20 cm and 20–40 cm), over three years. Factor Analysis (FA) and Discriminant Analysis (DA) were used to analyze the data. With FA, the observed variables were grouped into a smaller number of latent variables related to soil attributes. Discriminant Analysis was used to classify and identify the most dominant attributes and indicators for the time and space variability of soil parameters. The FA performed for the surface layer included factors related to texture, water and nutrient retention capacity, chemical composition, and soil fertility. In the sub-surface layer, the factor structure was similar, with four factors related to texture, chemical composition, nutrient availability, and soil fertility. The most influential factors and variables in temporal discrimination (sampling dates) in both layers were those related to chemical composition, with electric conductivity as the preponderant indicator. As for the spatial differentiation (fields), the dominant factor in the surface layer was texture, and in the sub-surface layer, nutrient availability. The most important discriminant indicators of spatial variability were fine sand proportion and available potassium, respectively, for the surface and sub-surface layers. The results obtained showed potential for the multidimensional and integrated assessment of patterns of temporal and spatial variation of soil functions from agricultural practices or soil degradation processes.

Applying the correct dose of nitrogen (N) fertilizer to crops is extremely important. The current predictive models of yield and soil–crop dynamics during the crop growing season currently combine information about soil, climate, crops, and agricultural practices to predict the N needs of plants and optimize its application. Recent advances in remote sensing technology have also contributed to digital modelling of crop N requirements. These sensors provide detailed data, allowing for real-time adjustments in order to increase nutrient application accuracy. Combining these with other tools such as geographic information systems, data analysis, and their integration in modelling with experimental approaches in techniques such as machine learning (ML) and artificial intelligence, it is possible to develop digital twins for complex agricultural systems. Creating digital twins from the physical field can simulate the impact of different events and actions. In this article, we review the state-of-the-art of modelling N needs by crops, starting by exploring N dynamics in the soil−plant system; we demonstrate different classical approaches to modelling these dynamics so as to predict the needs and to define the optimal fertilization doses of this nutrient. Therefore, this article reviews the currently available information from Google Scholar and ScienceDirect, using relevant studies on N dynamics in agricultural systems, different modelling approaches used to simulate crop growth and N dynamics, and the application of digital tools and technologies for modelling proposed crops. The cited articles were selected following the exclusion criteria, resulting in a total of 66 articles. Finally, we present digital tools and technologies that increase the accuracy of model estimates and improve the simulation and presentation of estimated results to the manager in order to facilitate decision-making processes.