Browsing by Author "Fernando Cebola Lidon"
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- Digitization of Crop Nitrogen Modelling: A ReviewPublication . Luís Silva; Luís Alcino Conceição; Fernando Cebola Lidon; Manuel Patanita; Paola D’Antonio; Costanza FiorentinoApplying 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.
- Measuring the Influence of Key Management Decisions on the Nitrogen Nutritional Status of Annual Ryegrass-Based Forage CropsPublication . Luís Silva; Sofia Barbosa; Fernando Cebola Lidon; José Santos-Silva; Luís Alcino ConceiçãoIncreasing nitrogen use efficiency (NUE) by improving agricultural practices and soil knowledge, and implementing precision agriculture, is essential to reduce the overuse of fertilizers and increase nutrient retention. This study aimed to optimize N management in agriculture by establishing a critical N dilution curve (CNDC) and analyzing variations in NUE and the N nutrition index (NNI) among different crops under various treatments. Using a Bayesian model, the CNDC was determined as %Nc = 3.63 × PDM−0.71. The results showed that plant dry matter (PDM) and plant N content (PNC) varied significantly with crop type and sampling moments. Strong positive correlations are presented by PDM with N uptake (NUp) (0.89) and NNI (0.88), along with an inverse correlation with critical N concentration (−0.95). The study found that crops under irrigation conditions had higher NUp and higher NNI. This study provides valuable insights into the influence of key management decisions on the N nutritional status of annual ryegrass-based forage crops. The results highlight the critical role of accurate and conscious decision-making in improving NUE and crop yields, emphasizing the complex interactions between biomass production and N dynamics in crops. The conclusions allow significant benefits to be realized, contributing to the sustainability of agricultural systems.
- Reflectance-based assessment of nitrogen status in ryegrass and mixed ryegrass-clover intercropping fodder cropsPublication . Luís Silva; Sofia Barbosa; Teresa Carita; Paola D’Antonio; Fernando Cebola Lidon; Luís Alcino ConceiçãoEffective nitrogen (N) management is essential for optimizing crop yields and minimizing environmental impacts, particularly in semi-arid regions where climate risks and natural resource constraints complicate decisionmaking. These low-energy systems require precise N strategies tailored to their unique challenges. This study evaluated a sensor-driven data analysis workflow for assessing N status in ryegrass-based fodder crops under semi-arid conditions and identified the most effective bands and vegetation indices (VIs) for use. Field trials conducted at Herdade da Comenda in Portugal employed a split-plot design, testing three N topdressing rates (0, 120, and 200 kg ha⁻¹) across varying crop types and irrigation systems. Both physical and remote measurements of crop parameters and N nutrition indicators were taken to address the limitations of current approaches in these conditions. The study found that vegetation pixels dominate spectral imagery, making additional filtering, such as ExG masks, unnecessary at ryegrass tillering and stem-elongation in ryegrass-based fodders. This simplification reduces processing time, costs, and digital footprints. Key VIs—NDRE, RERVI, and CIRE—proved robust for monitoring variables such as crop type, growth stage, and N treatments, showing strong correlations with N status indicators (NNI and CNI). Additionally, the study contrasted the efficiency of the entirely remote NNI method with the enhanced accuracy of the hybrid CCCI-CNI approach, providing valuable insights for tailored N management in semi-arid systems.