The requirements for a good stand in a no-till field are the same as those for conventional planting as well as added field and machinery management. Among the various factors that contribute towards producing a successful maize crop, seed depth placement is a key determinant. Although most no-till planters on the market work well under good soil and residue conditions, adjustments and even modifications are frequently needed when working with compacted or wet soils or with heavy residues. The main objective of this study, carried out in 2010, 2011 and 2012, was to evaluate the vertical distribution and spatial variability of seed depth placement in a maize crop under no-till conditions, using precision farming technologies and conventional no-till seeders. The results obtained indicate that the seed depth placement was affected by soil moisture content and forward speed. The seed depth placement was negatively correlated with soil resistance and seeding depth had a significant impact on mean emergence time and the percentage of emerged plants. Shallow average depth values and high coefficients of variation suggest a need for improvements in controlling the seeders’ sowing depth mechanism or more accurate calibration by operators in the field.

Resumo O BRAMS-SFIRE é um novo sistema de modelação atmosférica com componente de propagação de fogo desenvolvido no Centro de Previsão de Tempo e Estudos Climáticos (CPTEC / INPE) no Brasil em colaboração com o Instituto Mediterrâneo de Agricultura, Ambiente e Desenvolvimento (MED) em Portugal. O presente artigo descreve a incorporação do modelo de propagação de fogo no Brazilian developments on the Regional Atmospheric Modeling System (BRAMS). Os principais objetivos foram desenvolver o acoplamento entre um modelo atmosférico e um modelo de propagação de fogo que simulasse os efeitos da circulação atmosférica sobre um incêndio florestal e vice-versa. Esta ferramenta tem como objetivo ajudar a entender as relações entre o fogo e a paisagem nas florestas de carvalho mediterrâneas, e avaliar os resultados das simulações deste acoplamento na escala fina no ecossistema do Montado na região do Alentejo. Para isso, três grades de alta resolução espacial ao longo de três incêndios foram configuradas com dados de caracterização de superfície realistas e propriedades dos modelos de combustível. Uma grade foi colocada sobre fogo em uma planície e as outras nas montanhas, para avaliar os diferentes tipos de propagação do fogo. Este trabalho demonstra que este sistema simula de forma consistente a interação entre o fogo, os modelos de combustível e a atmosfera, mostrando que o fogo altera a circulação local ao nível da superfície, intensifica as correntes de vento ascendentes e descendentes, alterando a estrutura da atmosfera.

Water content is one of the most critical characteristics in plant physiological development. Therefore, this information is a crucial factor in determining the water stress conditions of vegetation, which is essential for assessing the wildfire risk and land management decision-making. Remote sensing can be vital for obtaining information over large, limited access areas with global coverage. This is important since conventional techniques for collecting vegetation water content are expensive, time-consuming, and spatially limited. This work aims to evaluate the vegetation live fuel moisture content (LFMC) seasonal variability using a multiscale remote sensing approach, particularly on rockroses, the Cistus ladanifer species, a Western Mediterranean basin native species with wide spatial distribution, over the Herdade da Mitra at the University of Évora, Portugal. This work used four dataset sources, collected monthly between June 2022 and July 2023: (i) Vegetation samples used to calculate the LFMC; (ii) Vegetation reflectance spectral signature using the portable spectroradiometer FieldSpec HandHeld-2 (HH2); (iii) Multispectral optical imagery obtained from the Multispectral Instrument (MSI) sensor onboard the Sentinel-2 satellite; and (iv) Multispectral optical imagery derived from a camera onboard an Unmanned Aerial Vehicle Phantom 4 Multispectral (P4M). Several temporal analyses were performed based on datasets from different sensors and on their intercomparison. Furthermore, the Random Forest (RF) classifier, a machine learning model, was used to estimate the LFMC considering each sensor approach. MSI sensor presented the best results (R² = 0.94) due to the presence of bands on the Short-Wave Infrared Imagery region. However, despite having information only in the Visible and Near Infrared spectral regions, the HH2 presents promising results (R² = 0.86). This suggests that by combining these spectral regions with a RF classifier, it is possible to effectively estimate the LFMC. This work shows how different spatial scales, from remote sensing observations, affect the LFMC estimation through machine learning techniques.

The current Common Agriculture Policy (CAP) foresees a reduction of 50% in the use of herbicides by 2030. This study investigates the potential of integrating remote sensing with a low-cost RGB sensor and variable-rate technology (VRT) to optimize herbicide application in a ryegrass (Lolium multiflorum Lam.) fodder crop. The trial was conducted on three 7.5-hectare plots, comparing a variable-rate application (VRA) of herbicide guided by a prescription map generated from segmented digital images, with a fixed-rate application (FRA) and a control (no herbicide applied). The weed population and crop biomass were assessed to evaluate the efficiency of the proposed method. Results revealed that the VRA method reduced herbicide usage by 30% (0.22 l ha⁻¹) compared to the FRA method, while maintaining comparable crop production. These findings demonstrate that smart weed management techniques can contribute to the CAP’s sustainability goals by reducing chemical inputs and promoting efficient crop production. Future research will focus on improving weed recognition accuracy and expanding this methodology to other cropping systems.

Sorghum is a resilient crop important for sustainable intensification in semi-arid regions, yet the impact of variable seeding rates on its early development remains under-researched. This research investigated the early establishment of hybrid sorghum under three seeding strategies, ”Uniformise” (medium density across all zones), “Optimise” (increased density in low-soil apparent Electrical Conductivity (ECa)), and “Maximise” (increased density in high-soil ECa), at the Herdade da Comenda (Innovation Center—Elvas, Portugal). Crop performance was monitored over 33 days, the established window for safe direct grazing, using Unmanned Aerial Vehicle (UAV) multispectral imagery to derive the Normalised Difference Vegetation Index (NDVI) and Canopy Cover (Cveg), alongside physical sampling of plant height and biomass. Statistical analysis revealed that both the seeding strategy and soil variability significantly affected early growth. The “Uniformise” strategy recorded the highest plant height, NDVI, and Cveg values, whereas the “Optimise” strategy performed the poorest. Additionally, an accumulation of 407.5 Growing Degree-Days (GDDs; °C) accelerated the phenological cycle by five days relative to the climatological normal. Despite differences in vegetative vigour, no statistically significant variations were observed in final biomass across the strategies. These results indicate that while the “Uniformise” approach provided a more balanced environment for early establishment under these specific Mediterranean conditions, the lack of biomass differentiation highlights the potential for resource optimisation. The study demonstrates that UAV-based remote sensing is a useful diagnostic tool to identify these spatial limitations, providing the data to refine variable-rate seeding (VRS) algorithms and improve the economic efficiency of precision sowing.