The agricultural sector is currently confronting multifaceted challenges such as an increased food demand, slow adoption of sustainable farming, a need for climate-resilient food systems, resource inequity, and the protection of small-scale farmers’ practices. These issues are integral to food security and environmental health. Remote sensing technologies can assist precision agriculture in effectively addressing these complex problems by providing farmers with high-resolution lenses. The use of vegetation indices (VIs) is an essential component of remote sensing, which combines the variability of spectral reflectance value (derived from remote sensing data) with the growth stage of crops. A wide array of VIs can be used to classify the crops and evaluate their state and health. However, precisely this high number leads to difficulty in selecting the best VI and their combination for specific objectives. Without thorough documentation and analysis of appropriate VIs, users might find it difficult to use remote sensing data or obtain results with very low accuracy. Thus, the objective of this review is to conduct a critical analysis of the existing state of the art on the effective use of VIs for the discrimination and monitoring of several important agricultural crops (wheat, corn, sunflower, soybean, rape, potatoes, and forage crops), grasslands and meadows. This data could be highly useful for all the stakeholders involved in agricultural activities. The current review has shown that VIs appear to be suitable for mapping and monitoring agricultural crops, forage crops, meadows and pastures. Sentinel-1 and Sentinel-2 data were the most utilized sources, while some of the frequently used VIs were EVI, LAI, NDVI, GNDVI, PSRI, and SAVI. In most of the studies, an array of VIs needed to be employed to achieve a good discrimination of crops or prediction of yields. The main challenges in using VIs are related to the variation of the spectral characteristics during the vegetation period and to the similarities of the spectral signatures of various crops and semi-natural meadows. Thus, further studies are needed to establish appropriate models for the use of satellite data that would prove to have greater accuracy and provide more relevant information for the efficient monitoring of agricultural crops.

Although essential for achieving high crop yields required for the growing population worldwide, nitrogen, (N) in large amounts, along with its inefficient use, results in environmental pollution and increased greenhouse gas (GHG) emissions. Therefore, improved nitrogen use efficiency (NUE) has a significant role to play in the development of more sustainable crop production systems. Considering that wheat is one of the major crops cultivated in the world and contributes in high amounts to the large N footprint, designing sustainable wheat crop patterns, briefly defined by us in this review as the 3 Qs (high quantity, good quality and the quintessence of natural environment health) is urgently required. There are numerous indices used to benchmark N management for a specific crop, including wheat, but the misunderstanding of their specific functions could result in an under/overestimation of crop NUE. Thus, a better understanding of N dynamics in relation to wheat N cycling can enhance a higher efficiency of N use. In this sense, the aim of our review is to provide a critical analysis on the current knowledge with respect to wheat NUE. Further, considering the key traits involved in N uptake, assimilation, distribution and utilization efficiency, as well as genetics (G), environment (E) and management (M) interactions, we suggest a series of future perspectives that can enhance a better efficiency of N in wheat.

Soil salinization processes have increased over the years and affect large parts of agricultural fields. The purpose of this review was to highlight the most important aspects regarding the potential effects of soil salinity on plants. In the current context of climate change, extreme weather and increased drought periods can lead to plant metabolic dysfunctionalities and accumulation of salt ions because of the increasing need for irrigation. The most important limiting factor, salinity, has a highly negative impact on plant growth independent of the appearance of either natural or anthropic status. The negative aspects include decreased leaf development rate, a low water level in all parts of the plant, reduced cell division and elongation, and low-intensity photosynthetic rate. Other negative aspects are directly related to stomata closure, reduced transpiration, low CO₂ level, and limitations on seed germination. However, there are also some positive aspects to the presence of salinity in soil. The field offers unlimited possibilities of research in order to activate pathways that help plants become resistant to salt stress. Several physiological parameters can benefit from low salt concentration (halopriming), such as germination, vigor, rapid seedling growth, and increased stomata number. Further studies should focus on both the positive and negative aspects of the increase in soil salinity.

Arbuscular mycorrhizal fungi (AMF) are beneficial for plant development and help absorb water and minerals from the soil. The symbiosis between these fungi and plant roots is extremely important and could limit crop dependence on fertilizers. The aim of this study was to evaluate the influence of AMF on tomatoes (Solanum lycopersicum L.), based on important agronomic traits of vegetative biomass, production, and fruits. The experiment was conducted in high tunnels, using 12 tomato genotypes under three different treatments: T1, control, without fertilizer and mycorrhizae colonization; T2, fertigation, without mycorrhizae colonization; and T3, arbuscular mycorrhizal fungi (AMF), seedling roots being inoculated with specialized soil-borne fungi. Plant growth, yield and fruit parameters indicated better results under mycorrhizal treatment. Root colonization with fungi varied significantly depending on the treatment and genotype, with a variation of 6.0–80.3% for frequency and 2.6–24.6% for intensity. For a majority of characteristics, the mycorrhization (T3) induced significant differences compared with the T1 and T2 treatments. In addition, AMF treatment induced a different response among the genotypes. Among the elements analyzed in the soil, significant differences were observed in phosphorous levels between planting the seedlings and after tomato harvesting and clearing of the plants. The results suggest that reducing fertilizers and promoting the symbiotic relationships of plants with soil microorganisms may have beneficial consequences for tomato crops.

The agricultural sector is currently confronting multifaceted challenges such as an increased food demand, a slow adoption of sustainable farming, a need for climate-resilient food systems, resource inequity, and protection of the small-scale farmers’ practices, all issues integral to food security and environmental health. Remote sensing technologies can assist precision agriculture to effectively address these complex problems, by providing farmers with a high-resolution lens. The use of vegetation indices (VIs) is an essential component of remote sensing, which combine the variability of spectral reflectance value (derived from remote sensing data) with the growth stage of crops. Currently a wide array of VIs is available that could be used to provide a classification and an evaluation of the state and health of crops. However precisely this high number leads to difficulties in selecting the best VI and combination of VIs for a specific objective. Without a thorough documentation and analysis of appropriate VIs, users might be confronted with difficulties in using remote sensing data or even with a very low accuracy of the results. Thus, the objective of this review is to conduct a critical analysis of the existing state of the art on the most important features related to the effective use of VIs for the discrimination and monitoring of the most important agricultural crops (wheat, corn, sunflower, soybean, rape, potatoes, and forage crops), grasslands and meadows. This data could be highly useful for all the stakeholders involved in agricultural activities (from farmers, researchers up to institutions dealing with the centralization and monitoring of agricultural crops).

During the process of maize seed production, in order to ensure the genetic purity of parental forms of hybrid maize, an important work performed is the removal of male inflorescences from plants on mother rows. Hand detasseling has high precision but is labor-intensive. Mechanical detasseling offers the possibility to cover large acreages in a short period of time, but the number of leaves removed has a varying influence on plant performance and seed yield. The aim of this study was to simulate three types of damages on plants similar to those induced through mechanical detasseling and to assess the effects for five inbred lines during the course of three years. Results show that when tassels alone were removed, the average seed yield decreased an average of 4–21%. When two leaves were removed with the tassel, yield decreased an average of 22–31%, while when plants were cut above the main ear, seed yield decreased an average of 31–66%. Environmental conditions influenced seed yield, especially high temperatures during flowering. Yield response to tassel and leaves removal varied between the inbred lines. Genotype controls maize ear and kernel characters, while environmental factors exercise a strong influence on seed yield, due to the succession of years with contrasting weather conditions in a key phenophase. Within the trend of full mechanization in agriculture, identification of inbred lines that cope better with plant damage can assist in optimizing seed production.