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Analysis of Meteorological Influencing Factors and Machine Learning Prediction of Wild Morel Yield in Gannan Prefecture, China
(2026-05-01)
Kong, Dejiang
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He, Shuling
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Yongsiri, Ploypailin
Morchella (morels) are rare edible fungi with high economic value but challenging cultivation requirements. Traditional yield prediction methods rely on manual records and statistics, which are time-consuming and yield low accuracy due to morels' long growth cycles, complex environmental factors, and insufficient documentation. This study presents a novel machine learning approach for predicting wild morel yields in Gannan Prefecture, China, using ten years (2013-2023) of monthly meteorological data. Random Forest was applied for feature selection, and Gradient Boosting Machine (GBM) was used to address zero-yield data. The resulting datasets were then employed to develop predictive models with six machine learning algorithms, namely Random Forest, Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), Transformer, and Convolutional Neural Network (CNN). Comparative analysis revealed that the GBM-LSTM hybrid model achieved superior performance (MSE: 8047.40, RMSE: 90.43, MAE: 60.90, R<sup>2</sup>: 0.81). Feature importance analysis identified air humidity (0.298) as the most critical factor affecting morel yields, followed by oxygen concentration, rainfall, light intensity, air quality, CO2 concentration, and average low temperature. Climate trend analysis over the past decade indicates that environmental deterioration, including an increase in temperature (+1.2 °C), a decrease in rainfall (-8.3%), and a reduction in humidity (-6.7%), has been accompanied by a decline in wild morel production, highlighting the vulnerability of this valuable species to climate change. These findings provide scientific guidance for optimizing cultivation strategies and developing climate-adaptive management practices for sustainable morel production.
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Annual analysis of surface temperature, land cover, and precipitation impacts on durian harvests in Thailand
(2026-01-01)
Lubis, Muhammad Zainuddin
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Dwi Wulandari, Pratiwi
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Simanjuntak, Andrean V.H.
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Batara, B.
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Kausarian, Husnul
This study analyzes the impact of land surface temperature (LST), NDVI, land cover, precipitation, and climatic factors on durian productivity in Thailand (2019–2025). Results show a significant positive correlation between LST and durian yields, particularly in the southern regions, where LST increased by 0.2–0.3 °C. Statistical models indicate that the southern sector will contribute over 50% of the national durian harvest by 2025, with an average yield of 617,990 tons. The precipitation and NDVI trends also correlate with regional variations in yield. The results underscore the complex interactions of temperature, land cover, and precipitation, emphasizing the need for targeted adaptation strategies and integrated planning to sustain agricultural productivity. These findings highlight the critical role of climate variables in shaping durian productivity and stress the importance of adaptive strategies to ensure sustainable agricultural output.
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Satellite-derived Spatio-temporal Dynamics of Sea Surface Temperature in the Indonesian and Halmahera Seas During ENSO Events
(2025-03-01)
Lubis, Muhammad Zainuddin
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Purwanto, Budi
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Sobaruddin, Dyan Primana
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Adrianto, Dian
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Dwinovantyo, Angga
Our study investigates the satellite-derived spatio-temporal dynamics of sea surface temperature (SST) in the Indonesian and Halmahera Seas from 2019 to 2021, highlighting its implications for climate change and marine resource management. SST values range from 21.95ºC to 33.50ºC, with pronounced peaks during the East Season (June to October) and lower temperatures in the West Season (January to March). These variations are closely associated with the El Niño-Southern Oscillation (ENSO) and seasonal wind and rainfall patterns. During the East Season, we observed notable upwelling events that indicate significant ecological impacts on fish distribution and fisheries productivity. Our study employed Conductivity-Temperature-Depth (CTD) data to validate satellite observations from the Copernicus Marine Environment Monitoring Service (CMEMS). The results revealed a strong correlation between satellite and observation data (coefficients of 0.91 and 0.93), confirming the reliability of satellite data for monitoring SST in remote marine areas. Our findings underscore the critical importance of continuous SST monitoring for sustainable marine resource management and the integration of satellite data in oceanographic studies. Our study is vital for developing adaptive strategies to address climate variability, particularly El Niño and La Niña events, which significantly influence regional weather patterns and ocean dynamics and ultimately impact global climate systems. Future research should explore the long-term trends of SST toward ongoing climate change and the resilience of marine ecosystems in the face of such variability.
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Impact of Climate Change on Livestock Production and Its Adaptation in Nepal
(2025-01-01)
Sedai, Dilli Ram
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Gyawali, Saroj
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Dangal, Megh Raj
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Yuangyai, Chumpol
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Lim, Noor Hashimah Hashim
Climate change directly affects livestock by increasing the temperature, which leads to increased infertility, loss of conception, poor expression of heat, repeat breeding, and sterility. This results in loss of weight gain, lower feed conversion ratio, increased transmission of vector-borne diseases, incidence and distribution of external parasites, and diseases susceptible to livestock systems. Livestock can be distressed by altering external factors. A study was conducted using a review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Environmental management is documented as a significant instrument for the review protocol. A systematic search process on the impact of climate change adaptation measures on livestock production includes enclosure and elimination criteria for suitability valuation. It is presented through quality assessment, data achievement, concepts, and investigations. Relevant literature was retrieved using Scopus, Google Scholar databases, and Web of Science. The effects of climate change on Nepalese livestock producers include reducing heat stress, risk management, adopting technology development by farmers, animal management, breeding management, feed and feeding management, and manure management for adaptation. These strategies should be applicable at the nationwide and local levels as development approaches. These conclusions and variations are relevant for improving farmers’ economies and ensuring food security in the least developed and developing countries with livestock producers.
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Projected Climate Change Effects on Global Vegetation Growth: A Machine Learning Approach
(2023-12-01)
Nguyen, Kieu Anh
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Seeboonruang, Uma
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Chen, Walter
In this study, a machine learning model was used to investigate the potential consequences of climate change on vegetation growth. The methodology involved analyzing the historical Normalized Difference Vegetation Index (NDVI) data and future climate projections under four Shared Socioeconomic Pathways (SSPs). Data from the Global Inventory Monitoring and Modeling System (GIMMS) dataset for the period 1981–2000 were used to train the machine learning model, while CMIP6 (Coupled Model Intercomparison Project Phase 6) global climate projections from 2021–2100 were employed to predict future NDVI values under different SSPs. The study results revealed that the global mean NDVI is projected to experience a significant increase from the period 1981–2000 to the period 2021–2040. Following this, the mean NDVI slightly increases under SSP126 and SSP245 while decreasing substantially under SSP370 and SSP585. In the near-term span of 2021–2040, the average NDVI value of SSP585 slightly exceeds that of SSP245 and SSP370, suggesting a positive vegetation development in response to a more pronounced temperature increase in the near term. However, if the trajectory of SSP585 persists, the mean NDVI will commence a decline over the subsequent three periods (2041–2060, 2061–2080, and 2080–2100) with a faster speed than that of SSP370. This decline is attributed to the adverse effects of a rapid temperature rise on vegetation. Based on the examination of individual continents, it is projected that the NDVI values in Africa, South America, and Oceania will decline over time, except under the scenario SSP126 during 2081–2100. On the other hand, the NDVI values in North America and Europe are anticipated to increase, with the exception of the scenario SSP585 during 2081–2100. Additionally, Asia is expected to follow an increasing trend, except under the scenario SSP126 during 2081–2100. In the larger scope, our research findings carry substantial implications for biodiversity preservation, greenhouse gas emission reduction, and efficient environmental management. The utilization of machine learning technology holds the potential to accurately predict future changes in vegetation growth and pinpoint areas where intervention is imperative.
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Coral Reef Bleaching under Climate Change: Prediction Modeling and Machine Learning
(2022-05-01)
Boonnam, Nathaphon
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Udomchaipitak, Tanatpong
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Puttinaovarat, Supattra
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Chaichana, Thanapong
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Boonjing, Veera
The coral reefs are important ecosystems to protect underwater life and coastal areas. It is also a natural attraction that attracts many tourists to eco-tourism under the sea. However, the impact of climate change has led to coral reef bleaching and elevated mortality rates. Thus, this paper modeled and predicted coral reef bleaching under climate change by using machine learning techniques to provide the data to support coral reefs protection. Supervised machine learning was used to predict the level of coral damage based on previous information, while unsupervised machine learning was applied to model the coral reef bleaching area and discovery knowledge of the relationship among bleaching factors. In supervised machine learning, three widely used algorithms were included: Naïve Bayes, support vector machine (SVM), and decision tree. The accuracy of classifying coral reef bleaching under climate change was compared between these three models. Unsupervised machine learning based on a clustering technique was used to group similar characteristics of coral reef bleaching. Then, the correlation between bleaching conditions and characteristics was examined. We used a 5-year dataset obtained from the Department of Marine and Coastal Resources, Thailand, during 2013–2018. The results showed that SVM was the most effective classification model with 88.85% accuracy, followed by decision tree and Naïve Bayes that achieved 80.25% and 71.34% accuracy, respectively. In unsupervised machine learning, coral reef characteristics were clustered into six groups, and we found that seawater pH and sea surface temperature correlated with coral reef bleaching.
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Wavelet relationship between climate variability and deep groundwater fluctuation in Thailand’s Central Plains
(2018-02-01)
Seeboonruang, Uma
Groundwater is constantly under direct and indirect pressures from the anthropogenic effects, long-term climate change, and climate variability. This research investigates the association, in the time-frequency domain, between the groundwater fluctuations in Thailand’s Lower Chao Phraya Basin and specific climate variability forces: the El Nino/Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Asian Summer Monsoons (ASM). The analysis was carried out using the wavelet method and the findings presented in the form of the complete, global, and local wavelet spectrums. In addition, the Pearson correlation was utilized to establish the linkages between the groundwater and the climate variability forces. The results indicated that the deep groundwater signals of the Lower Chao Phraya Basin were linked to the ENSO, IOD and ASM with the absolute correlation coefficients in excess of 0.5. Moreover, the recent climatic indices exerted greater influence on the groundwater than in the past, given the former’s correlation coefficients of 0.9 on average. By comparison, the deep groundwater was strongly associated with the recent ENSO and ASM but weakly linked to the IOD, with the absolute coefficients of around 0.5. The findings revealed the resilience of the deep groundwater under such high frequency signal conditions as the seasonal and tidal oscillations. Moreover, the results showed that the groundwater could be an alternative source of water supply during periods of droughts in the region.

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