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.

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.

Assessing the efficiency of nitrogen application is a valuable first step in establishing an optimum N fertilizer management plan. The aim of this study was to elaborate a sustainable N management system for sown grassland installed in temperate regions, through manipulation of nitrogen use efficiency and nitrogen response efficiency (NRE) indicators. Research was performed in western Romania. The biological material consists in two forage species of major importance for the forage base worldwide, namely Trifolium pratense L. and Lolium multiflorum Lam., in pure stands and in mixture. Both forage species were sown at two different spacings between rows (12.5 and 25.0 cm) and were fertilized with nitrogen (N) in three doses (N0-control, N50-fertilized with 50 kg N ha⁻¹ and N100-fertilized with 100 kg N ha⁻¹). The studies were made in 2016 and 2017, years very different from each other in matter of rainfalls. The results revealed a different behavior of the three sward types analyzed: while in Trifolium pratense monoculture and in the mixture of the two species studied, less N was removed than was applied; on the other hand Lolium multiflorum monoculture used a high proportion of the N applied, being at the limit to access N from the organic pool or residual N from fertilizer applications to a preceding crop.

Identifying regional variations and agronomical records can turn into a function of magnitude and timing for sustainable maize production. The crop characteristics are directly related with the temperature and precipitation from the growing season. Therefore, the main aim of this study was to highlight maize crop requirements from the most suggestive records from Romania. A literature search was performed based on keywords related to both maize crop and agrometeorological requirements. After the evaluation of the temperature requirements on maize phenology’s principal stages, some inconsistencies were identified. These are related to the leaf development stage and the development of the fruit stage, where the minimum temperature interval overlaps with the optimum. A wide variety of assessments were also made, taking into account mainly the monthly temperature and precipitation requirements. The number of principal growth stages assessed differed greatly from one source to another. The growth degree days has been incoherently expressed and calculated, and these details must be taken into account for developing further models. The results regarding phenology aim to propose the setting up of comparable records at a regional (PannEx area) and global scale. This review will help to develop new climate projections in Romania in the climate change context through the project Agroclim.