Climate change and its impact on agricultural production due to the occurrence of extreme weather events appear to be more imminent and severe than ever, presenting a global challenge that necessitates collective efforts to mitigate its effects.There have been many practical and modelling studies so far to estimate the extent of climate change and possible damages on agricultural production, suggesting that water availability may decrease by 50% and agricultural productivity between 10 and 30% in the coming years ahead. Though there have been many studies to estimate the possible level of damage by the climate change on the production of many agricultural crops, no study has been conducted on the greenhouse tomato production. Therefore, this study was conducted to discover the effects of extreme high temperatures during the 2022-2023 growing season on the high-tech Turkish tomato greenhouse industry through a survey. The results showed that all greenhouses lost yield, ranging from 6 to 53%, with an average of 12.5%. Survey data revealed that irrigation and fog system water consumption increased by 29.32% and 31.42%, respectively, while fertilizer and electricity consumption rose by 23.66% and 19%. Some 76.5% of the growers declared difficulty in climate control, 11.7% reported tomato cluster losses with no information on yield loss, 9% experienced yield losses despite no cluster losses, and 61.7% observed a decline in tomato quality, leading to reduced sales prices. Considering these findings, it is recommended that greenhouses must adopt advanced climate control technologies, expand fog system capacities, and integrate renewable energy sources to enhance resilience against climate-induced challenges. Additionally, improving water-use efficiency, optimizing cooling strategies, using new and climate-resistant varieties and adjusting cropping seasons could help mitigate yield losses due to extreme temperatures. The study results offer extremely valuable insights into greenhouse production for researchers, technology developers, and policymakers for the mitigation of climate change effects and the development of more sustainable production systems.

Changing climate and shifts in weather patterns have significantly affected food production systems, which is evident in the form of crop damage, reduced yield, and market instability. Water- and chemical-intensive agriculture practices have made the sector a major contributor of carbon emissions, affecting the global climate, nutrient cycling, food security, etc. The adoption of climate-smart agriculture practices can develop agricultural systems that effectively balance agricultural productivity and food security, and contribute to climate change mitigation. The present study is a synthesis of datasets from 116 published articles to assess the changes in soil and its carbon stocks while transitioning from conventional to climate-smart agricultural practices (CSA) in India. The effects of these practices in different edaphic and environmental conditions across the country have also been studied. The meta-analysis of the data was performed using OpenMEE and Jamovi software. Further, a review of existing literature on the impact of CSA practices on crop yield has also been presented. Conservational tillage, integrated nutrient management, and agroforestry-based systems increased the SOC buildup rate by 17.1%, 25.9%, and 39.2%, respectively, compared to the conventional agriculture practices. Climatic factors (temperature and precipitation); edaphic factors (soil pH, depth, and texture); and experiment duration significantly influence the sequestration potential of agroecosystems. Based on the results, the present study concludes that CSA practices curb CO2 emissions and improve soil quality and crop yield along with sequestering carbon. These practices, therefore, offer a win-win strategy for socio-economic development and achieving the target of net-zero emissions by 2070.

The traditional method of detecting crop nutrients is based on the direct chemical detection method in the laboratory, which causes great damage to crops. In order to solve the above problems, the main goal of this study is to design a precise fertilization method for greenhouse vegetables based on the improved back-propagation neural network (IM-BPNN) algorithm to increase fertilizer utilization efficiency, reduce production costs, and improve the economic viability of agriculture. First, soil samples from the farm in china are selected. With the laboratory treatment, available phosphorus, available potassium, and alkaline nitrogen are extracted. These data are preprocessed by the z-score (zero-mean normalization) standardization method. Then, the BPNN (backpropagation neural network) algorithm is improved by being trained and combined with the characteristics of the dual particle swarm optimization algorithm. After that, the soil sample data are divided into training and test sets, and the model is established by setting parameters, weights, and network hierarchy. Finally, the NBTY (nutrient balance target yield),BPNN (backpropagation neural network) and IM-BPNN algorithm are used to calculate the amount of fertilizer. Compared with the BPNN and NBTY algorithm, it shows that the IM-BPNN algorithm can more accurately determine the amount of fertilizer required by vegetables and avoid over-application, which can improve fertilizer utilization efficiency, reduce production costs, and improve the economic feasibility of agriculture.

Modern agriculture faces the dual challenge of ensuring sustainability while meeting the growing global demand for food. Smart agriculture, which uses data from the environment and plants to deliver water exactly when and how it is needed, has attracted significant attention. This approach requires precise water management and highly accurate real-time monitoring of crop water stress. Existing monitoring methods pose challenges such as the risk of plant damage, costly sensors, and the need for expert adjustments. Therefore, a low-cost, highly accurate water stress estimation model was developed that uses deep learning and commercially available sensors. The model uses the relative stem diameter as a water stress index and incorporates data from environmental sensors and an RGB camera, which are processed by the proposed daily normalization. In addition, domain adaptation in our Transformer model was implemented to enable robust learning in different areas. The accuracy of the model was evaluated using real cultivation data from tomato crops, achieving a coefficient of determination (R2) of 0.79 in water stress estimation. Furthermore, the model maintained a high level of accuracy when applied to different areas, with an R2 of 0.76, demonstrating its high adaptability under different conditions.

- This research proposes a solution to improve the system for monitoring relevant environmental parameters using sensors for flood mitigation. Sensors are used to collect data regarding farm flood situation. The collected data are trained for a classification model to activate the solar-powered water pump to mitigate flood incidents in a flood-prone area. The system helps farmers to monitor real-time environmental parameters relevant to farming operations and flood including soil moisture level, water level, and water flow speed in a nearby canal that provides water to the farm. To reduce flood damage, the system assists to drain the excessive water to prevent prolonged submerging of the crop. The devices are designed to use the electricity from solar power, so the system is practically used outdoor where an electricity cord is difficult to setup. Experimental results show that the sensing data from the deployed sensors are accurate. The generated prediction models give the high performance with average of 1.0, 0.97, and 0.93 F-1 score for no-flooding, mild-flooding, and severe flooding respectively.