Ondrasek Gabrijel, Rathod Santosha, Manohara Kallakeri Kannappa, Gireesh Channappa, Anantha Madhyavenkatapura Siddaiah, Sakhare Akshay Sureshrao, Parmar Brajendra, Yadav Brahamdeo Kumar, Bandumula Nirmala, Raihan Farzana, Zielińska-Chmielewska Anna, Meriño-Gergichevich Cristian, Reyes-Díaz Marjorie, Khan Amanullah, Panfilova Olga, Seguel Fuentealba Alex, Romero Sebastián Meier, Nabil Beithou, Wan Chunpeng Craig, Shepherd Jonti, Horvatinec Jelena
Faculty of Agriculture, The University of Zagreb, Svetosimunska c. 25, 10000 Zagreb, Croatia.
ICAR-Indian Institute of Rice Research, Hyderabad 500030, India.
Plants (Basel). 2022 Mar 8;11(6):717. doi: 10.3390/plants11060717.
Salinization of soils and freshwater resources by natural processes and/or human activities has become an increasing issue that affects environmental services and socioeconomic relations. In addition, salinization jeopardizes agroecosystems, inducing salt stress in most cultivated plants (nutrient deficiency, pH and oxidative stress, biomass reduction), and directly affects the quality and quantity of food production. Depending on the type of salt/stress (alkaline or pH-neutral), specific approaches and solutions should be applied to ameliorate the situation on-site. Various agro-hydrotechnical (soil and water conservation, reduced tillage, mulching, rainwater harvesting, irrigation and drainage, control of seawater intrusion), biological (agroforestry, multi-cropping, cultivation of salt-resistant species, bacterial inoculation, promotion of mycorrhiza, grafting with salt-resistant rootstocks), chemical (application of organic and mineral amendments, phytohormones), bio-ecological (breeding, desalination, application of nano-based products, seed biopriming), and/or institutional solutions (salinity monitoring, integrated national and regional strategies) are very effective against salinity/salt stress and numerous other constraints. Advances in computer science (artificial intelligence, machine learning) provide rapid predictions of salinization processes from the field to the global scale, under numerous scenarios, including climate change. Thus, these results represent a comprehensive outcome and tool for a multidisciplinary approach to protect and control salinization, minimizing damages caused by salt stress.
自然过程和/或人类活动导致的土壤和淡水资源盐碱化已成为一个日益严重的问题，影响着生态系统服务和社会经济关系。此外，盐碱化危及农业生态系统，在大多数栽培植物中引发盐胁迫（养分缺乏、pH值和氧化应激、生物量减少），并直接影响粮食生产的质量和数量。根据盐/胁迫的类型（碱性或pH中性），应采用特定的方法和解决方案来就地改善这种情况。各种农业水利技术措施（水土保持、少耕、覆盖、雨水收集、灌溉和排水、控制海水入侵）、生物措施（农林业、间作、种植耐盐品种、接种细菌、促进菌根、用耐盐砧木嫁接）、化学措施（施用有机和无机改良剂、植物激素）、生物生态措施（育种、脱盐、应用纳米产品、种子生物引发）和/或制度性解决方案（盐度监测、国家和区域综合战略）对盐碱化/盐胁迫及许多其他制约因素非常有效。计算机科学（人工智能、机器学习）的进展能够在包括气候变化在内的众多情景下，从田间到全球尺度快速预测盐碱化过程。因此，这些结果代表了一种多学科方法的全面成果和工具，用于保护和控制盐碱化，将盐胁迫造成的损害降至最低。
