About this Research Topic
This research topic is focused on the most recent advances in foliar nutrient analysis as a reliable tool to assess the nutritional needs of crops. Compositional nutrient diagnosis is a relatively new alternative approach to plant nutrient analysis that aims to provide a more comprehensive understanding of the nutrient status of plants. Statistical methods, modeling and other novel approaches offer promise as tools and strategies for managing plant nutrition and crop fertilization more effectively, in the frame of sustainable agriculture. In short, the goal of this special issue is to: a) review and summarize the science and practice of foliar nutrient analysis and other alternative approaches and their use in improving nutrient application efficiency, resulting in positive effects in terms of field production and fruit quality, b) provide useful information on how to maximize farmers’ income, and the nutritional/nutraceutical value of horticultural products, and c) minimize the negative environmental impacts due to nutrient abuse.
In this Research Topic, we welcome articles covering the following themes:
• Sustainable fertilization practices;
• Advances in foliar nutrient analysis protocols and techniques as a reliable agronomic guide to optimize nutrient application rates;
• Precision agriculture approaches in evaluation of plant nutritional status and fertilization needs of crops;
• Recent interdisciplinary advances between sustainable fertilization and enhancement of field productivity;
• Updated information from data interrelating sustainable crop nutrition and fruit qualitative characteristics-benefits for human health
Keywords: sustainable, nutrient, fruit, optimum, fertilization
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.