Efficient and sustainable water management remains a critical challenge in organic agriculture, where input use is restricted and irrigation decisions must be carefully calibrated to avoid resource waste and crop stress. This study presents a practical and cost-effective solution combining Internet-of-Things (IoT) sensors with artificial intelligence (AI) algorithms to enhance the performance of drip irrigation systems in organic farming contexts. The proposed system integrates capacitive soil moisture sensors, temperature probes, and flow meters into a field-deployable network communicating via LoRaWAN. Sensor data, collected at 15-minute intervals, are transmitted to a cloud platform that also integrates localized weather forecasts and field-specific agronomic data, including soil characteristics and crop phenological stages. After data cleaning and noise reduction using Kalman filtering, the input stream is fed into a hybrid machine learning model combining Long Short-Term Memory (LSTM) neural networks and Random Forest regression. The model is retrained periodically to ensure robustness under dynamic field conditions. Based on the 48-hour irrigation forecasts, the system autonomously adjusts irrigation timing and duration through solenoid valve control, maintaining soil moisture within optimal ranges. The approach was field-tested on two organic vegetable farms (tomato and bell pepper) in southeastern Romania during the 2024 growing season. Compared to traditional irrigation scheduling, the system reduced total water use by 27% and increased crop yield by 15%, with a measurable improvement in water-use efficiency (from 5.8 to 7.1 kg/m³). These results validate the effectiveness of IoT- and AI-based systems for precision irrigation in small- to medium-scale organic farms. The solution demonstrates tangible benefits in resource conservation, productivity, and climate resilience, and offers a replicable model for enhancing decision-making in data-constrained agroecological systems.

This study explores the future of fruit and vegetable crop improvement in the Middle East through the integration of traditional breeding techniques with modern agricultural science. Extensive experimental trials were conducted on apricot, sour cherry, tomato, basil, and several other species across diverse ecological zones ranging from the Mediterranean coast (Istanbul) to the mountainous Alborz region (Azerbaijan, Damavand). Soil-based experiments examined the effects of various irrigation types, fertilization regimes, and environmental stress factors. The results revealed remarkable changes in plant resilience, yield quality, and adaptive traits, suggesting that combining ancestral knowledge with modern precision agriculture can significantly enhance crop productivity under shifting climatic conditions. These findings offer strategic insights for future breeding programs aimed at sustaining food security in semi-arid and Mediterranean regions of the Middle East.

Google Earth Engine (GEE) has revolutionized remote sensing. The GEE cloud platform lets users quickly analyze large satellite imagery datasets with custom programs, enhancing global-scale analysis. Crop condition monitoring using GEE would greatly help in decision-making and precision agriculture. Estimating canopy chlorophyll content (CCC) is an effective method for monitoring crops through remote sensing because chlorophyll in leaves is a key indicator. A hybrid model that combines radiative transfer models (RTMs), such as PROSAIL, with Gaussian Process Regression (GPR) can effectively estimate crop biophysical parameters using remote sensing images. GPR has proven to be one of the best methods for this purpose. This study aimed to develop a hybrid model to estimate CCC from S2 imagery and transfer it to the GEE platform for efficient data processing. In this work, the CCC (g/cm2) data from the S2 biophysical processor toolbox for the S2 imagery of ICAR-Indian Agricultural Research Institute (IARI) on February 23rd, 2023, was used as observation data to train the hybrid algorithm. The hybrid model was successfully validated against the 155 input data with an R2 of 0.94, RMSE of 10.02, and NRMSE of 5.04%. The model was integrated into GEE to successfully create a CCC estimated map of IARI using S2 imagery from February 23, 2023. An R2 value of 0.96 was observed when GEE-estimated CCC values were compared against CCC values estimated locally. This establishes that the GEE-based CCC estimation with the PROSAIL+GPR hybrid model is an effective and accurate method for monitoring vegetation and crop conditions over large areas and extended periods.

Survival, growth and yield responses of salt-tolerant rice varieties were evaluated under seawater irrigation at various growth stages. Four Salinas (salt-tolerant) varieties (Rc 182, RC 186, Rc334, and RC326), a farmer’s variety (Rc 10), and susceptible check (IR 29) were exposed to pure seawater with 50.97 dS/m EC and irrigated with 50 liters per 100 cm² for three consecutive days at vegetative (BBCH 21-beginning of tillering), reproductive (BBCH 51-beginning of panicle emergence), and ripening (BBCH 85-soft dough) stages. Treatments flooded with seawater at the reproductive stage (panicle initiation) was most severely affected, with a high number of dead leaves, a lower leaf area, and a fifty percent (50%) reduction in filled grains. Days to maturity was shortened by a week, and sodium accumulation in roots and straw was higher. Exposure to seawater at the vegetative stage delayed maturity by one week compared to control plants. Theh varieties Rc 182, Rc 186, Rc 334, and Rc 326 were found to be suitable in seawater intrusion areas if exposure can be limited at the vegetative and ripening stages. Planting dates, even for NSIC-approved salt- tolerant varieties, should be adjusted to avoid exposure to salinity stress during the critical reproductive stage.

Salt stress is a major abiotic constraint limiting rice productivity in coastal and inland regions of Ghana, particularly at the seedling stage, where it severely impairs plant establishment and growth. Despite its significance, to the best of our knowledge, no salt-tolerant rice varieties have been formally released in Ghana. This study aimed to evaluate the seedling-stage responses of ten rice genotypes to varying levels of salinity stress and identify genotypes for future breeding efforts. A 4 × 10 factorial experiment was conducted in a Randomized Complete Block Design (RCBD) with six replications, under rain-sheltered conditions at the CSIR-Crops Research Institute, Kumasi, Ghana. Ten rice genotypes were exposed to four salinity treatments (0, 75, 100, and 125 mM NaCl) for 21 days. Key parameters, including shoot and root length, fresh and dry biomass, leaf area, and salinity tolerance indices, were assessed. Tolerance was determined using visual scoring, an analysis of variance, and eight calculated indices: the Fresh Weight Stress Tolerance Index (FWSI), Dry Weight Stress Tolerance Index (DWSI), Root Length Salinity Index (RLSI), Shoot Length Salinity Index (SLSI), Salinity Tolerance Index (STI), Salinity Susceptibility Index (SSI), Tolerance Index (TI), and Percent Reduction. Significant genotypic variation was observed under salt stress. ARICA 11 exhibited the highest FWSI (29.19) and STI (1.28), indicating superior tolerance, followed by LEGON 1 and HR32051F1-2-33-1. In contrast, AGRA and AGYAPA recorded the lowest tolerance indices. Salinity stress substantially inhibited growth, especially at 125 mM, where complete seedling mortality was observed. Survival declined progressively with increasing salinity: six genotypes survived at 75 mM, five at 100 mM, and none at 125 mM, highlighting a threshold for genotype resilience. This study provides evidence of moderate salt tolerance among selected genotypes and confirms the absence of highly tolerant varieties under severe salinity. The findings underscore the urgent need for breeding programs targeting salinity resilience in rice to sustain productivity in salt-prone areas of Ghana.

Jackfruit (Artocarpus heterophyllus Lam.) is monoecious, cross-pollinated, and highly heterozygous. Propagation through seeds is not widely accepted because seeds from one tree do not produce fruit true to the type of the parent plant and are recalcitrant. Jackfruit is still considered a difficult fruit species to propagate by vegetative means through grafting, and the graft survival percentage is only 30.00% - 55.00%. Grafted seedlings of jackfruit var. EVIARC Sweet (3-4 months old after grafting) were acquired from the Plant Propagation Nursery of the Department of Horticulture, Visayas State University, Visca, Baybay City Leyte, and were raised in the screenhouse and were used as donor plants. This study was conducted to evaluate the effect of plant growth regulators, thidiazuron (TDZ), 6-benzylaminopurine (BAP), and indole-3-butyric acid (IBA) at varying concentrations on in vitro shoot induction and multiplication of jackfruit. Shoot tips of grafted seedlings were used as the source of explants. Standard MS (Murashige & Skoog, 1962) solid medium was used as the basal medium during the inoculation period. One week after inoculation, the cultures were transferred to full MS media strength supplemented with various plant growth regulators (PGRs). There were 17 treatments and four (4) replications with 15 sample explants per replication arranged in CRD. The results showed that the earliest shoot formation occurred with the application of 0.1 mg/L thidiazuron (TDZ) + 3.0 mg/L benzyl aminopurine (BAP) and 0.5 mg/L TDZ + 5.0 mg/L BAP, taking 17.38 and 18.88 days, respectively. The highest shoot induction rate (83.75%) was observed in the combination of 0.1 mg/L TDZ + 3.0 mg/L BAP, producing 4.00 shoots per explant. The shoot length was also longer (3.12 cm) when applying 0.1 mg/L TDZ + 3.0 mg/L BAP. The combination of TDZ and BAP also led to more internodes, nodes, and leaves than other treatments. A multiplication rate of 2-5 shoots per explant was achieved after the first subculture. Further research is needed to explore the effects of other PGR combinations or alternative cytokinin sources that might further enhance jackfruit propagation.

Stripe rust caused by Puccinia striiformis f.sp. tritici (Pst) is threatening wheat production in Ethiopia. Wheat varieties succumb to new Pst race(s) soon after their release from research centers. This stusy aimed to determine prevalent Pst races and identify resistance sources in the Ethiopian wheat landraces. A total of 44 Pst samples were collected for race analysis from the Amhara and Oromia regions. The field test was conducted at Kulumsa and Meraro, while the seedling tests were conducted at Kulumsa in a greenhouse. Seven Pst phenotypic races were identified from 44 samples, and PstS11 was the most frequent (36.4%), whereas three distinct Pst races were identified from 24 samples by SSR genotyping. Of the 103 wheat landraces (69 bread and 34 durum), 57 exhibited resistance across both locations and seasons. The 57 Ethiopian wheat landraces that showed field resistance were further exposed to three Pst races at the seedling stage, and 32 Ethiopian wheat landraces exhibited seedling resistance to all races. The resistance in these materials will be genotyped and used in the wheat breeding program.

In the saline soils of Karakalpakstan, the agronomic potential of the nitrogen-fixing strain Azotobacter chroococcum K2020, isolated from the indigenous microbial community, was evaluated under field conditions. The aim of this study was to assess the effect of this strain on the growth, development, and yield performance of several cotton (Gossypium hirsutum L.) varieties.
Due to its ability to fix atmospheric nitrogen, A. chroococcum serves as a promising tool for sustainable and environmentally friendly agricultural practices. The reduction in the use of synthetic nitrogen fertilizers not only improves soil health but also enhances ecological sustainability.
A field experiment was conducted at the research site of the Institute of Genetics and Experimental Plant Biology using a randomized complete block design (RCBD) with two replications. The tested cotton varieties included “Bukhara-102,” “Gulbahor-2,” “UzRFA-709,” and “UzRFA-710.” Seeds were inoculated with a bioformulation based on the A. chroococcum K2020 strain and compared to non-inoculated controls.
The results demonstrated that inoculation stimulated both vegetative and reproductive development. The highest efficacy was observed in the “UzRFA-710” variety, which showed the greatest plant height and number of bolls. Plant height measurements were as follows:
“UzRFA-710”—125.18 ± 0.81 cm, “UzRFA-709”—118.64 ± 1.22 cm, “Gulbahor-2”—120.22 ± 1.44 cm, and “Bukhara-102”—110.40 ± 1.61 cm.
Improvements in fiber quality and yield were also recorded following inoculation. The observed biological effects are attributed to the strain’s nitrogen-fixing capacity, synthesis of phytohormones (auxins and gibberellins), and enhancement of plant resistance to abiotic stresses.
Thus, the application of Azotobacter chroococcum K2020 represents a promising direction for the development of eco-friendly biotechnologies and may contribute significantly to sustainable cotton production.

Climate variability poses significant challenges to agricultural systems, particularly in semi-arid regions where smallholder farmers depend on reliable yields and efficient resource use. Considering the potential of ecological intensification, this study investigated the intercropping of maize with paddy straw mushroom (Volvariella volvacea) as a strategy to optimize the microclimate and improve land use efficiency. Field experiments were conducted at the Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore, during the summer and kharif seasons of 2022, using a randomized block design comprising nine treatments. These included four maize planting geometries—wide row spacing (60 × 25 cm), close row spacing (45 × 25 cm), wide paired rows (45/75 × 25 cm), and close paired rows (30/60 × 25 cm)—each tested with and without organic mulch (T1 to T8), along with a polyhouse control (T9). Microclimatic variables such as air and soil temperature, relative humidity, and bed moisture were continuously monitored to assess their influence on mushroom growth and yield. The results showed that close maize spacing (45 × 25 cm), particularly when combined with organic mulch (T6), created a favorable microenvironment with moderated temperatures and increased humidity. This treatment accelerated mushroom development, reducing the cropping duration to 22 days, and resulted in higher biological efficiency and yield compared to wider spacings without mulch. Although the polyhouse control (T9) produced the highest yield, its high infrastructure cost limits feasibility for smallholder farmers. Optimal microclimatic thresholds for mushroom cultivation were identified as 26 to 29 °C in the mornings and 29 to 33 °C in the afternoons, with relative humidity between 80 and 98 percent. All intercropping treatments achieved land equivalent ratio greater than one, indicating improved productivity per unit area. The study demonstrates that simple, field-level interventions aligned with local climatic conditions can mitigate environmental stress, enhance intercrop viability, and promote climate-resilient and resource-efficient agriculture in vulnerable agroecosystems.

Wheat production is largely reduced by salinity stress around the world. High soil salinity that is likely to worsen with increasing climate change decreases the root–shoot growth and tissues, and, eventually, leads to the destruction of wheat crops. Plants show substantial variation in salinity tolerance depending on the species and growth stages. As Triticum species is mostly sensitive to soil salinity, it is important to develop and grow salt-tolerant wheat genotypes. However, modern hexaploid wheat holds less genetic variation, lacking the potential alleles required for their adaptability towards high salinity. Thus, it is crucial to assess the genetic diversity of different germplasms for the selection of potential genotypes with desirable traits against stressed conditions. Bread wheat comprises A, B, and D subgenomes, where Triticum boeoticum, T. monococcum, and T. urartu are considered as ancestral species of their ‘A’ sub-genome. As ‘A’ genomes of diploid wheat species share homology to ‘A’ sub-genomes of hexaploid wheat, they can facilitate the transfer of desirable traits in wheat breeding programs. Here, we screened 31 diploid A-genome wheat genotypes belonging to four species to understand their response towards control and salinity stress (150 mM NaCl) conditions in terms of growth parameters. Interestingly, domesticated T. monococcum genotypes in the experiment showed higher tolerance to salinity stress compared to genotypes of T. aegilopoides, T. boeoticum, and T. urartu. Salinity stress equally affected the root and shoot tissues of the studied species. The identified tolerant genotypes may serve as a good candidate to introgress the salinity tolerance trait in different wheat species.

Acknowledgements: We are thankful to TUBITAK 1001 (No. 123R072) for providing funding to perform this research work.