The increasing demand for agricultural land is driving deforestation, which contributes to rising greenhouse gas emissions—a primary driver of climate change. Agroforestry systems present a valuable alternative approach for mitigating these emissions. This study investigates the influence of various agroforestry systems on key parameters for assessing soil carbon dynamics, including soil carbon stock, microbial biomass carbon (MBC), and microbial respiration (MR) in northern Thailand. The study compared different agroforestry systems, such as coffee monoculture (CA), coffee intercropped with forest trees (CF), coffee intercropped with persimmon trees (CP), and natural forest (NF), as reference areas. Soil samples were collected from three depths (0–20, 20–40, and 40–60 cm) across three seasons (rainy, cold, and summer). Results showed that soil carbon from land use changes from NF to coffee agroforestry systems led to a 12.10% increase in CF and an 11.89% decrease in CP. The CA system showed a non-significant 9.06% decrease compared to the natural forest. MBC levels were higher in the agroforestry coffee areas (CF and CP), comparable to those in NF. However, CA exhibited significantly lower MBC (p < 0.05) compared to other treatments. While MBC and MR showed a positive correlation, higher MBC did not necessarily indicate increased microbial activity or significant dissolved organic carbon (DOC) accumulation. Environmental factors, particularly seasonal variations, significantly influenced the results across all three investigations. This study demonstrates that both agroforestry coffee systems (CF and CP) more effectively mitigate deforestation impacts and enhance soil fertility compared to coffee monoculture. For carbon stock enhancement specifically, CF emerged as the optimal choice.

The increasing demand for agricultural land is driving deforestation, which contributes to rising greenhouse gas emissions—a primary driver of climate change. Agroforestry systems present a valuable alternative approach for mitigating these emissions. This study investigates the influence of various agroforestry systems on key parameters for assessing soil carbon dynamics, including soil carbon stock, microbial biomass carbon (MBC), and microbial respiration (MR) in northern Thailand. The study compared different agroforestry systems: coffee monoculture (CA), coffee intercropped with forest trees (CF), coffee intercropped with persimmon trees (CP), and natural forest (NF) as reference areas. Soil samples were collected from three depths (0–20, 20–40, and 40–60 cm) across three seasons (rainy, cold, and summer). Results showed that soil carbon from land-use changes from NF to coffee agroforestry systems led to a 12.10% increase in CF and an 11.89% decrease in CP. The CA system showed a non-significant 9.06% decrease compared to the natural forest. MBC levels were higher in the agroforestry coffee areas (CF and CP), comparable to those in NF. However, CA exhibited significantly lower MBC (p < 0.05) compared to other treatments. While MBC and MR showed a positive correlation, higher MBC did not necessarily indicate increased microbial activity or significant dissolved organic carbon (DOC) accumulation. Environmental factors, particularly seasonal variations, significantly influenced the results across all three investigations. This study demonstrates that both agroforestry coffee systems (CF and CP) more effectively mitigate deforestation impacts and enhance soil fertility compared to coffee monoculture. For carbon stock enhancement specifically, CF emerged as the optimal choice.

Vertical farming (VF) allows for more plants to be cultivated in a particular land area, maximizing land-use efficiency. VF employs multileveled shelves enablin Vertical farming (VF) allows for more plants to be cultivated in a particular land area, maximizing land-use efficiency. VF employs multileveled shelves enablin

Pesticides are widely applied in agriculture to protect crops from pests and diseases, but their residues can pose risks to human health. This study developed a low cost, non destructive hyperspectral imaging approach using a mobile phone to detect profenofos residues. Filter paper samples were used as a preliminary experiment before application on agricultural produce. Samples were prepared at seven concentrations and measured inside a lighting box under visible light (400-700 nm), with images captured using three mobile phone camera resolutions (13, 32, and 64 MP). Digital filters (580-780 nm) simulated hyperspectral area scanning, and RGB values were extracted from each image.Six machine learning algorithms, including support vector machine, partial least squares discriminant analysis, artificial neural network, decision tree, random forest, and k nearest neighbors, were evaluated using leave one out cross validation and an independent blind test set. Results showed the 32 MP camera gave the most consistent performance, with the neural network reaching a mean cross validation accuracy of 94.0% and 100% accuracy on blind testing, showing accuracy did not increase monotonically with resolution. This low cost, mobile phone method shows potential for rapid, on site, non destructive pesticide residue detection.