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.

MicroRNAs regulate plant responses to fungal infections and immunity. In this study, miRNAs were identified in six rice cultivars during a Rhizoctonia solani Kühn AG1-IA infection using a deep sequencing approach. Known and novel miRNAs were analyzed in these rice cultivars, and a set of fungal infection/immunity-associated miRNAs and target genes were quantified by reverse transcription (RT)-qPCR in six rice cultivars. Additionally, the relative expression of these miRNAs was analyzed in different time points of the infection, wild species of rice, and in response to different strains of R. solani. Osa-miR1320-5p showed preferential expression during the fungal infection in all the six rice genotypes, while Osa-miR156d, Osa-miR159b, Osa-miR820c, and Osa-miR1876 were differentially regulated in susceptible and resistant genotypes. A greater degree of downregulation of miRNAs was observed during the initial time points of infection (24–72 h), suggesting a maximum molecular activity of rice-R. solani interaction and resistance response of the host during the early phase of infection. After R. solani infection, the expression of Osa-miR820c and Osa-miR156d was downregulated in Oryza rufipogon, O. alta, O. latifolia, and O. minuta, while Osa-miR397b was downregulated in all the wild rice species except O. officinalis. This study provided comprehensive information on the repertoire of miRNAs expressed in six sheath blight disease-susceptible and resistant indica and aus rice cultivars.

A robust forecast of rice yields is of great importance for medium-to-long-term planning and decision-making in cereal production, from regional to national level. Incorporation of spatially correlated adjacent effects in forecasting models in general, results in accurate forecast. The Space Time Autoregressive Moving Average (STARMA) is the most popular class of model in linear spatiotemporal time series modelling. However, STARMA cannot process nonlinear spatiotemporal relationships in datasets. Alternately, Time Delay Neural Network (TDNN) is a most popular machine learning algorithm to model the nonlinear pattern in data. To overcome these limitations, two-stage STARMA approach was developed to predict rice yield in some of the most intensive national rice agroecosystems in India. The Mean Absolute Percentage Errors value of proposed STARMA-II approach is lower compared to Autoregressive Moving Average (ARIMA) and STARMA model in all examined districts, while the Diebold-Mariano test confirmed that STARMA-II model is significantly different from classical approaches. The proposed STARMA-II approach is promising alternative to classical linear and nonlinear spatiotemporal time series models for estimating mixed linear and nonlinear patterns and can be advanced tool for mid-to-long-term sustainable planning and management of crop yields and patterns in agroecosystems, i.e., food supply and demand from local to regional levels.

In agroecosystems, drought is a critical climatic phenomenon that affects evapotranspiration and induces water stress in plants. The objective in this study was to characterize and forecast water stress in the Hyderabad region of India using artificial intelligence models. The monthly precipitation data for the period 1982–2021 was characterized by the standardized precipitation index (SPI) and modeled using the classical autoregressive integrated moving average (ARIMA) model and artificial intelligence (AI), i.e., artificial neural network (ANN) and support vector regression (SVR) model. The results show that on the short-term SPI3 time scale the studied region experienced extreme water deficit in 1983, 1992, 1993, 2007, 2015, and 2018, while on the mid-term SPI6 time scale, 1983, 1991, 2011, and 2016 were extremely dry. In addition, the prediction of drought at both SPI3 and SPI6 time scales by AI models outperformed the classical ARIMA models in both, training and validation data sets. Among applied models, the SVR model performed better than other models in modeling and predicting drought (confirmed by root mean square error—RMSE), while the Diebold–Mariano test confirmed that SVR output was significantly superior. A reduction in the prediction error of SVR by 48% and 32% (vs. ARIMA), and by 21% and 26% (vs. ANN) was observed in the test data sets for both SPI3 and SPI6 time scales. These results may be due to the ability of the SVR model to account for the nonlinear and complex patterns in the input data sets against the classical linear ARIMA model. These results may contribute to more sustainable and efficient management of water resources/stress in cropping systems.

To improve grain yield under direct seeded and aerobic conditions, weed competitive ability of a rice genotype is a key desirable trait. Hence, understanding and dissecting weed competitive associated traits at both morphological and molecular level is important in developing weed competitive varieties. In the present investigation, the QTLs associated with weed competitive traits were identified in BC₁F_2:3population derived from weed competitive accession ofO. glaberrima(IRGC105187) andO. sativacultivar IR64. The mapping population consisting of 144 segregating lines were phenotyped for 33 weed competitive associated traits under direct seeded condition. Genetic analysis of weed competitive traits carried out in BC₁F_2:3population showed significant variation for the weed competitive traits and predominance of additive gene action. The population was genotyped with 81 genome wide SSR markers and a linkage map covering 1423 cM was constructed. Composite interval mapping analysis identified 72 QTLs linked to 33 weed competitive traits which were spread on the 11 chromosomes. Among 72 QTLs, 59 were found to be major QTLs (> 10% PVE). Of the 59 major QTLs, 38 had favourable allele contributed from theO. glaberrimaparent. We also observed nine QTL hotspots for weed competitive traits (qWCA2a,qWCA2b,qWCA2c,qWCA3,qWCA5, qWCA7,qWCA8,qWCA9, andqWCA10) wherein several QTLs co-localised. Our study demonstratesO. glaberrimaspecies as potential source for improvement for weed competitive traits in rice and identified QTLs hotspots associated with weed competitive traits.

This study was conducted to characterize new plant type (NPT) traits among 650 genetically diverse rice genotypes of tropical japonica and indica and to establish an initial core set for NPT traits. Analysis of variance revealed highly significant differences among the genotypes for all the traits assessed except flag length and width and leaf angles. Dendrogram categorized the genotypes into five distinct duration groups. Genotypes viz., Pumphamah, IRGC5097, IRGC37015, IRGC43741, IRGC50448, IRGC53089, IRGC39111, IRGC18021, Haorei Machang, IRGC44069, IRGC8269, Thangmoi, IRGC33130 and IRGC29772 were identified as possessing strong culm. Long panicles with a length of more than 35 cm were found in IRGC8269, IRGC9147, IRGC14694, IRGC19642, IRGC27435, IRGC39111, IRGC31051, IRGC26011and IRGC25892. Ideal leaf angle of NPT genotypes of 5°, 10° and 20° of flag leaf, 1^st and 2^nd leaves was not found in any genotype but with a combination of 5°, 10° and 10° was observed in IRGC63102 and IRGC66644. NPT flag leaf length and width of 50 and 2 cm, respectively, was seen in ‘Kemenya Kepeu’ and ‘IRGC29772’. High grain number of more than 350 was observed in IRGC53089, IRGC31063 and Azhoghi. A total of 72 genotypes were found with a combination of one or more ideal plant type traits of which, hierarchical cluster analysis based on genetic distances selected 32 as NPT core set. This core set will serve as an ideal genetic resource for breeding programs aimed at NPT development.