Global warming and temperature rise are now recognized as one of the biggest problems faced by humanity. Even a 1ºC rise in temperature affects all living beings, including crops. Heat stress affects crop physiology from seed germination to fruit abscission and senescence. Some crops and plants have the potential to withstand heat stress and exhibit this ability to varying degrees through a series of biochemical and molecular changes. This review critically examines the impact of high-temperature stress on key physiological processes in fruit crops, including photosynthesis, reproductive development, assimilate partitioning, and yield formation. It also emphasizes how phytohormonal interaction, metabolic modifications, and stress-responsive signalling pathways contribute to thermotolerance. With a focus on their use in identifying heat-tolerant characteristics, genes, and rootstock-scion pairings in fruit crops, recent developments in omics approaches, genomics, transcriptomics, proteomics, metabolomics, and phenomics are described. Furthermore, based on current research, management-based mitigation techniques such as grafting, cultivar selection, antitranspirants, and water and canopy management techniques are assessed. This review outlines knowledge gaps and future research goals for creating heat-resilient fruit production systems under changing climatic conditions by integrating traditional physiological knowledge with new omics-driven insights. The synthesis presented here will be valuable for plant physiologists, horticulturists, breeders, and researchers working on climate adaptation in perennial fruit crops.
Introduction Wolffia globosa has been considered a functional food formulation due to its excellent nutritional value and rapid growth. Organic liquid fertilizer from fish (OLF) has been used as a source of nutrients for plant growth. However, there is limited information regarding the use of OLF for the cultivation of W. globosa . This research aimed to cultivate W. globosa using OLF during the monsoon season. Methods Two outdoor experiments were conducted during the monsoon of 2025. Experiment 1 comprised eight treatments of OLF, being 0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, and 3.5 mL/L. The plant was grown on rainwater in 11-L plastic containers covered with 50% black shade cloth and harvested at 11 days. Experiment 2 resembled the cultivation system in small-scale farms and comprised three treatments: 1.5, 2.5, and 3.5 mL/L, with OLF added during the experiment. OLF was made from fish waste collected from a local supermarket. The plant was grown on tap water in open 1.2-m diameter cement ponds. Biomass (g) was monitored every five days for 30 days. Daily rainfall was collected during the two experiments. Results Biomass gain was lower in Experiment 1 (1.8-2.4 times the starting mass) than in Experiment 2 (2.6-4.1 times the starting mass). Biomass was greater at higher rates of OLF in both experiments, although differences were not always statistically significant;biomass per unit area (g/m 2 ), average daily biomass gain (g/day), and daily productivity (g/m 2 /day) were greater at higher rates in Experiment 2. Biomass per unit area exceeded 2 kg/m 2 in treatment 3.5 mL/L after 30 days. In the three treatments, average daily biomass gain was the highest at 15 days (48.85-66.76 g/day), while daily productivity was the highest at five days (89.08-105.77 g/m 2 /day). Discussion OLF as high as 3.5 mL/L could fertilize W. globosa under the conditions of this study. Conclusion It is practical to cultivate W. globosa using OLF without changing media for a 30-day period. However, farmers may harvest the plant at 15 days, as average daily biomass gain reached a peak, and then restart the whole process of cultivation with the new media.
Introduction/Objective This study evaluated the effects of Ganga sludge-based organic manures on soil nutrient availability, growth, yield attributes, and land-use efficiency of wheat ( Triticum aestivum ), pea ( Pisum sativum ), and their intercropping system under a humid subtropical climate. Methods A field experiment was conducted using a randomized block design with seven nutrient management treatments, comprising a conventional fertilization control and single or combined applications of Jaivik Prom, Pori Potash, Dharti ka Chaukidar, Jaivik Poshak, and Jaivik Khad. Growth attributes, Crop Growth Rate (CGR), yield components, Land Equivalent Ratio (LER), competition indices, and correlations between soil properties and crop performance were evaluated under sole and intercropping systems. Data were analyzed using ANOVA and LSD tests ( p ≤ 0.05). Results Integrated organic nutrient management significantly enhanced crop performance compared with conventional fertilization. In pea, the combination of Pori Potash and Jaivik Poshak produced the highest grain yield under both intercropping (4.67 kg plot −1 ) and sole cropping (6.81 kg plot −1 ). In contrast, Jaivik Prom combined with Jaivik Khad markedly improved pod production and biomass. In wheat, the combined application of Pori Potash and Jaivik Khad resulted in the highest grain yield under sole (1.71 kg plot −1 ) and intercropping (1.00 kg plot −1 ), while Jaivik Khad alone enhanced crop growth rate, leaf area, and chlorophyll content. Pea performed better under sole cropping, whereas wheat benefited from intercropping. LER values (1.35–2.32) confirmed superior land-use efficiency of intercropping, and available phosphorus and potassium showed strong positive correlations with crop growth and yield. Discussion Improved crop performance was primarily associated with enhanced nutrient availability and complementary interactions between wheat and pea, particularly the legume component's facilitation of nitrogen availability. Conclusion The results indicate that integrated application of Ganga sludge-based organic manures, particularly combined formulations, offers a sustainable approach for nutrient recycling, soil fertility improvement, and enhanced productivity in organic and low-input cereal-legume systems.
Introduction/Objective Verticillium wilt, caused by Verticillium dahliae, is a persistent soilborne constraint to Mediterranean olive production. This cross-sectional study evaluated the association between culturable soil microsclerotial density and ordinal wilt stage across five Tunisian orchards and separately documented the analytical provenance of an IGS-targeted SYBR Green qPCR assay. Methods The retained dataset comprised one orchard-stage composite soil unit for each of four symptom stages in each orchard (20 units), with three technical plate counts per unit. Archived records do not preserve tree/core-level sampling metadata, which were not imputed. Stage effects were tested with orchard as a block, and an orchard-fixed-effects regression, cluster bootstrap, and influence analyses quantified the ordered trend and robustness. qPCR summaries were treated as ancillary assay information rather than field inoculum measurements. Results Mean inoculum increased from 8.4 MS/g at the asymptomatic stage to 168.8 MS/g at the severe/dead stage. The orchard-blocked stage effect was strong (F(3,12) = 79.98, P < 0.0001; partial η 2 = 0.952), and the adjusted trend was 0.421 log10 units per stage (95% CI, 0.351-0.491; R 2 = 0.922). Excluding the BH stage-3 value produced a similar slope (0.405; 95% CI, 0.331-0.478). Discussion The association was robust within this dataset, but the cross-sectional design and incomplete archived field-sampling metadata preclude causal or predictive interpretation. Observed stage-specific ranges are descriptive rather than validated thresholds. Conclusion Soil microsclerotial density was a strong local correlate of olive wilt stage. Predictive thresholds require prospective, independently replicated and spatially standardized validation.
Introduction Rapid climate change, soil degradation, and changing environmental conditions make crop selection difficult for farmers. This study proposes an IoT- and AI-based framework to recommend suitable crops using current soil conditions and future weather forecasts. It also identifies the key environmental factors influencing crop selection. Methods A three-layer architecture was designed with data collection, communication, and data processing modules. Real-time data on nitrogen, phosphorus, potassium, pH, temperature, and humidity were collected through IoT sensors and combined with rainfall and historical agricultural data. A dataset containing multiple environmental features and 22 crop classes was used for model development. Machine learning and deep learning methods, including Random Forest, XGBoost, K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Decision Trees (DT), Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM), were applied for classification and forecasting. Performance was evaluated using accuracy, F1-Score, MAE, RMSE, and R 2 . Pareto analysis was also performed to identify the most influential parameters. Results Random Forest and CNN achieved the highest classification accuracy of 99.54% with an F1-Score of 0.995, while XGBoost also performed strongly with 99.32% accuracy. Regression analysis showed that ensemble models outperformed linear models. Pareto analysis revealed that rainfall, humidity, and potassium were the most influential factors in crop recommendation. In a real-time case study, the framework recommended rice as the most suitable crop for the given input conditions. Discussion The results show that integrating IoT sensing with AI-based forecasting supports proactive crop planning before sowing and improves sustainable farming decisions under changing climate conditions. Conclusion The proposed framework effectively combines real-time monitoring, predictive analytics, and intelligent crop recommendation, offering a practical foundation for scalable precision agriculture systems.
