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.

Zinc malnutrition is a major issue in developing countries where polished rice is a staple food. With the existing significant genetic variability for high zinc in polished rice, the development of biofortified rice varieties was targeted in India with support from HarvestPlus, Department of Biotechnology, and Indian Council of Agricultural Research of Government of India. Indian Institute of Rice Research (IIRR) facilitates rice varietal release through All India Coordinated Rice Improvement Project (AICRIP) and also supports rice biofortification program in India. Various germplasm sets of several national institutions were characterized at IIRR for their zinc content in brown rice using energy-dispersive X-ray fluorescence spectroscopy indicating the range of zinc to be 7.3 to 52.7 mg/kg. Evaluation of different mapping populations involving wild germplasm, landraces, and varieties for their zinc content showed the feasibility of favorable recombination of high zinc content and yield. Ninety-nine genotypes from germplasm and 344 lines from mapping populations showed zinc content of ≥28 mg/kg in polished rice meeting the target zinc content set by HarvestPlus. Through AICRIP biofortification trial constituted since 2013, 170 test entries were nominated by various national institutions until 2017, and four biofortified rice varieties were released. Only the test entry with target zinc content, yield, and quality parameters is promoted to the next year; thus, each test entry is evaluated for 3 years across 17 to 27 locations for their performance. Multilocation studies of two mapping populations and AICRIP biofortification trials indicated the zinc content to be highly influenced by environment. The bioavailability of a released biofortified rice variety, viz., DRR Dhan 45 was found to twice that of control IR64. The technology efficacy of the four released varieties developed through conventional breeding ranged from 48 to 75% with zinc intake of 38 to be 47% and 46 to 57% of the RDA for male and female, respectively. The observations from the characterization of germplasm and mapping populations for zinc content and development of national evaluation system for the release of biofortified rice varieties have been discussed in the context of the five criteria set by biofortification program.

Organic agriculture (OA) is a continuously growing global concept that emphasizes the use of sustainable and environmentally-friendly practices. By adopting OA, it is possible to improve ecosystems services, increase biodiversity, decrease environmental pollution, reduce carbon footprints and mitigate greenhouse gas emissions, generating food that is free from harmful residues of agrochemicals, thereby enhancing food safety and security. This study provides a comprehensive review of the latest insights on the global utilization of land resources in OA, focusing particularly on some EU countries that experienced a notable and rapid progress in organic farming during the past two decades. With negligible 1.6% (75 Mha) of global cropland is currently dedicated to OA, there is ample opportunity to expand the adoption of OA and realize its multi-beneficial potential for farmers (by premium prices), and consumers (by healthier and nutritious food). The importance of OA has been recognized by the most recent EU agro-environmental policies and green strategies, with an ambitious goal to have at least 25% of agroecosystems under organic management by 2030. Despite numerous financial supports and a multifold increase in OA land area, many member states are unlikely to achieve this goal, including Croatia, which currently has a share of only 8% (~109,000 ha) of lands in OA. Furthermore, converting conventional land to organic farming has not always led to an increase in value-added final OA products. EU policies related to OA have been focused on the area of land cultivated organically, rather than overall production performance, and financial subsidies have been essential to achieve this policy. Therefore, some of critical obstacles and challenges for OA under rising pressures due to global climate change, public health and geopolitical crises need to be managed by specifically designed policies and regulations, which would contribute to more sustainable OA, i.e., food safety and security.

There is a close relationship among agriculture, economics, energy and the environment. A comparison was made between conventional and the system of rice intensification (SRI) methods of rice cultivation by conducting two experiments. One field experiment was conducted from 2013 to 2017 at 25 locations across India under the All India Coordinated Rice Improvement Project and another experiment was conducted in 2017 using surveys by collecting data from 262 randomly selected SRI farmers using a personal interview method in the Telangana state of India. The 5-year experimental data revealed that the SRI method of cultivation produced higher rice grain yield (up to 55%) compared to the conventional transplanting method. Survey data revealed that total costs of rice production reduced by 22.71% under SRI. Break even output under SRI was reduced by 58.1%. Adoption of SRI saved total energy inputs by 4350 MJ/ha. The energy productivities were 0.16 kg/MJ and 0.21 kg/MJ for conventional and SRI methods, respectively. Also, SRI resulted the lowest greenhouse gas emissions of 0.280 kg CO₂e/kg rice grain. Therefore, for ensuring higher productivity, net returns, energy efficiency and sustainable rice production it is recommended to adopt an environmentally friendly SRI method of crop establishment in the Telangana region of India.

Via national lockdowns, the COVID-19 pandemic disrupted the production and distribution of foodstuffs worldwide, including rice (Oryza sativa L.) production, affecting the prices in India’s agroecosystems and markets. The present study was performed to assess the impact of the COVID-19 national lockdown on rice prices in India, and to develop statistical machine learning models to forecast price changes under similar crisis scenarios. To estimate the rice prices under COVID-19, the general time series models, such as the autoregressive integrated moving average (ARIMA) model, the artificial neural network (ANN) model, and the extreme learning machine (ELM) model, were applied. The results obtained using the ARIMA intervention model revealed that during the COVID-19 lockdown in India, rice prices increased by INR 0.92/kg. In addition, the ELM intervention model was faster, with less computation time, and provided better results vs other models because it detects the nonlinear pattern in time series data, along with the intervention variable, which was considered an exogenous variable. The use of forecasting models can be a useful tool in supporting decision makers, especially under unpredictable crises. The study results are of great importance for the national agri-food sector, as they can bolster authorities and policymakers in planning and designing more sustainable interventions in the food market during (inter)national crisis situations.