Thrips are major pests affecting horticultural crops. Their effective and timely monitoring is essential for optimizing pesticide use and minimizing its environmental and economic costs. Sticky traps are commonly used to monitor thrips activity in greenhouses, but manually counting trapped thrips is labor-intensive, highlighting the necessity of a simple and time-efficient counting method. This study proposes a method for automatic counting of thrips on sticky traps from images acquired in a greenhouse using a general-purpose digital camera. This method involves two steps: extraction and classification of captured targets. In the first step, target images are automatically cropped via thresholding-based image processing. In the second step, a convolutional neural network (CNN) model is used to classify the objects in the cropped images as thrips or other objects. The CNN model was trained on trap images obtained under laboratory conditions using a scanner and trap images captured in a strawberry greenhouse using a digital camera. The counting accuracy was evaluated using images acquired from the greenhouse that were not used for training. The proposed method could count the number of thrips as accurately as or better (root mean squared error [RMSE] = 2.6 thrips, normalized RMSE [NRMSE] = 14.0%) than the widely used object-detection method YOLOv10 (RMSE = 2.7 thrips, NRMSE = 14.6%). Furthermore, its accuracy in counting thrips over time was evaluated. By training the CNN model with a dataset of trap images with high densities of thrips, the proposed method could accurately estimate the number of thrips for more than one month (RMSE = 4.6 thrips, NRMSE = 8.1%). This two-step approach enables the independent optimization of object extraction and classification processes. These results demonstrate that the proposed method is a promising tool for the effective monitoring of thrips infestations in agricultural settings.
Earthworms play a crucial role as ecosystem engineers in terrestrial ecosystems by influencing soil properties and contributing to carbon sequestration. Understanding their basic physiology is essential for comprehending their role in biogeochemistry and their responses to various substances such toxic chemicals and microplastics. This study investigated the relationship between the respiration rate and movement in earthworms using a dynamic measurement system. The respiration rate was measured using a flow-through chamber method, while movement was quantified by detecting movements of earthworms in recorded videos using a trained AI model (YOLOv8) and calculating the velocity of the center of detected earthworm area. The results showed a positive correlation between respiration rate and movement, with similar waveforms when the velocity of movement exceeded 2 mm s-1. On the other hand, no correlation was observed when the velocity was less than 2 mm s-1. Our findings partially supported the hypothesis that increased movement leads to higher respiration rates in earthworms. This study also highlights the limitations of the AI model in detecting earthworms and the potential influence of experimental conditions on their behavior. Further development of the methods used in this study is expected to advance our understanding of earthworm physiology and its implications for soil ecosystems.
Our previous research showed that increasing the thickness of a polyolefin (PO) film increases the longwave radiation absorptance (a), which in turn reduces the overall heat transfer coefficient (k). Moreover, the effect of the covering thickness on longwave radiation absorptance varies depending on the material type. Thus, the relationship between covering thickness (other than that of PO films) and heat insulation properties must be clarified to develop coverings with better heat insulation performance. This study aimed to clarify the relationship between covering thickness and k for several fluororesin films and polyethylene (PE) films and identify any differences between the materials. Additionally, comparisons were performed with PO data obtained in previous studies. Thickness was measured using a micrometer, a was measured using an emissivity meter, and k was calculated using an approximate formula based on our previous research. Thickness and k can be approximated by a linear function for all the test materials. The absolute value of the slope of the approximation equation for PE was smaller than the values for PO and fluororesin films. This implies that an increase in PE thickness is less likely to improve the heat insulation performance compared with increases in PO and fluororesin films.
This study aims to determine the effect of climate change on the irrigation water requirements (IWR) of widely grown crops (Alfalfa, Walnut, Sugar Beet, Cherry, Sunflower, Wheat, Grape (Wine), Maize (Silage), Canola, Barley) in Tekirdag with CROPWAT 8.0 model and to classify these crops in terms of irrigation strategies. To achieve this objective, firstly, climate data (1971-2024) were evaluated. Then, plant water consumption (ETc) and IWR were calculated and compared for each plant species for the 1971-2000 and 2001-2024 periods with CROPWAT 8.0. It was determined that there was a 9.9% increase in average temperatures and a 0.7% decrease in total annual precipitation in the 2001-2024 period compared to the 1971-2000 period. Compared to the 1971-2000 period, ETc (4.34%-6.93%) and IWR (7.86%-14.54%) of the most grown and produced crops in Tekirdag increased in the 2001-2024 period. In addition, according to irrigation strategies in the research area, crops were classified as i) the most critical crops in terms of irrigation (walnut, grape, cherry), ii) crops requiring optimization with irrigation strategies (maize, alfalfa, sugar beet), and iii) non-irrigation priority crops (wheat, sunflower, barley, canola). It is thought that the research will be a guide for planning water resources and implementing sustainable agriculture based on plant patterns with climate change.
Soil gas exchange plays a crucial role in biogeochemical cycles and is influenced by various environmental factors, particularly temperature. This study investigated the effects of temperature variation on the uptake of hydrogen (H2), carbon monoxide (CO), and methane (CH4) by volcanic ash soils from forests and arable land using a dynamic flow-through chamber system. The objective of this study was to elucidate how both short-period and long-period temperature changes influence gas uptake by these soils ("3 h temperature treatments" vs. "temperature treatments over 2 weeks to 2 months during the pre-incubation"). The study found that the uptake rates of H2 and CO were higher in forest soil than in arable soil. Across all long-period temperature treatments, H2 and CO uptake rates were highest at short-period temperatures of 25-35t within the tested range (-5-45t). CH4 uptake was lower than H2 and CO uptake, with forest soil taking up more CH4 than arable soil. The optimal temperature for CH4 uptake under 5-35t of short-period temperature with 25t of long-period temperature was 30t for both soil types. Heat stress had an irreversible effect on CH4 uptake, with a more pronounced decrease in the forest soil. The differing effects of short-period and long-period temperature treatments on soil uptake of these gases highlight the need to incorporate temperature variations across different timescales into soil gas exchange models. The findings also provide insights into soil gas exchange processes under temporary temperature increases caused by extreme weather events and diurnal changes.
Application of mulch is one technique to save water consumption and increase crop production. This study aimed to clarify effects of mulches and soil moisture conditions on biomass production, actual evapotranspiration (ET), and water use efficiency (WUE) of soybean [Glycine max (L.) Merr.]. Soybean was planted in clay pots with living mulch of white clover (CL) and shredded paper mulch (SP). Soil water content (SWC) in the pot was individually controlled at five levels by manual irrigation. The measured plant height, LAI, and the dry matter of the soybean were largest in the higher SWC with CL, and in the moderate SWC with SP, while the plant height of the clover in CL was higher in the lower SWC. The plant height, LAI, and the dry matter of the soybean were larger with SP compared with CL mostly in all SWC levels especially after the vegetative stage. The measured ET was significantly larger with CL compared with SP mostly in all SWC levels, e.g., the average ET in the whole period was 2.59 and 3.64 (mm/d) under the second wettest SWC level (33.6% and 33.4% in average) in SP and CL, respectively. One reason was considered as transpiration by the clover in CL. Another reason was the effect of reducing soil surface evaporation by SP that was demonstrated by the smaller ET in SP compared with measured evaporation from the bare soil pot particularly in the lowest SWC. The highest WUE for seed with SP (0.115 mg/g) was in the lowest SWC, while it was in the highest SWC with CL (0.035 mg/g). This study suggested that shredded paper mulch was effective for water saving cropping system with larger WUE compared with clover living mulch by a synergistic effect of the better growth of the soybean and the smaller ET.
The Agro-Meteorological Grid Square Data (AMGSD) system provides daily and hourly meteorological data for Japan's third-level mesh regions, supporting agricultural and environmental research. This study presents the methodology for generating hourly AMGSD by integrating observational data with Grid Point Value (GPV) provided by the Japan Meteorological Agency. Consistency between hourly and daily values is ensured through a unified computational process. Accuracy evaluations with five meteorological sites maintained by National Agriculture and Food Research Organization (NARO) indicate Root Mean Square Error (RMSE) values ranging from 0.6 to 1.3 degrees C (average 0.86 degrees C) for air temperature, 3.7 to 6.8% (average 5.3%) for relative humidity, and 20.0 to 23.1 W m-2 (average 21.1 W m-2 for downward longwave radiation (DLR). Sapporo site exhibited relatively larger temperature errors, likely due to differences in land use between the observation site and the nearest Japan Meteorological Agency (JMA) station. For forecast data, errors increased with longer lead times, with RMSE values for 9-day forecasts ranging from 1.6 to 3.3 degrees C for air temperature, 9.9 to 16.9% for relative humidity, and 17.0 to 37.9 W m-2 for DLR. In winter, colder regions (Sapporo and Morioka) showed slightly higher errors for relative humidity and DLR. Mean Error (ME) did not exhibit significant seasonal variations for air temperature or DLR, but in Morioka, relative humidity forecasts tended to be underestimated throughout the year.
Accurate leaf area density (LAD) is essential for radiative transfer simulations of forests, yet measurement methods are often limited to terrestrial LiDAR scanning (TLS). Numerous measurements are required to minimize occlusion; however, upper-canopy measurements are insufficient. Recently, new measurement methods such as handheld LiDAR scanning (HLS) and UAV-Li DAR have demonstrated the potential to reduce occlusion by enabling mobile data collection. However, the data acquired by UAV-LiDAR and HLS have not been directly used for radiative transfer calculations because their measurement accuracy is inferior to that of TLS, primarily because of the limitations of the inertial monitoring unit accuracy. The aim of this paper is to propose a radiance simulation model that considers the attenuation and reflection of direct irradiance within a forest canopy, utilizing LiDAR measurements. In the proposed approach, the point cloud is first converted into voxels. Then, the attenuation of the irradiance reaching an arbitrary voxel was calculated using the Beer-Lambert law with the point cloud between the light source and voxel as the input value. Finally, the radiance reflected in the direction of the sensor was calculated. The proposed method was applied to point clouds of a larch forest obtained through UAV-LiDAR and HLS, as well as to a combined dataset. The reliability of the proposed method was evaluated using the correlation coefficient (r) for the Sentinel-2 top of the atmosphere product red band reflectance. The results indicated that the r calculated from the radiance based on the UAV-LiDAR and coupled point cloud exceeded 0.75, indicating the validity of the method.
SARNet, a deep-learning model, was used to generate high-resolution (>32x) NDVI, RGB, and LST images based on Himawari meteorological satellite data, targeting application in the agricultural field. High-quality high resolution was achieved using NDVI data. However, limited resolution was achieved using RGB and LST data, owing to the extreme upscaling. Geographic factors such as cloud coverage, water coverage, and climatic zone significantly affected prediction accuracy. Retraining using local data is necessary when the climatic conditions are highly variable. The proposed approach exhibits the potential for achieving high-resolution satellite-based images for agricultural applications. However, further improvements in resolution and accuracy will require careful consideration of environmental conditions as well as further enhancements.
Estimating evapotranspiration is crucial for understanding Earth's energy and water budget, ecosystem dynamics, and for improved water resource management. The Evapotranspiration Index (ETindex) algorithm provides global terrestrial evapotranspiration information using affordable input data and a simple, fully automated computational procedure. The primary inputs of the algorithm are satellite-observed land surface temperature data and global weather information. The algorithm uses land surface temperature data from the Second-Generation Global Imager (SGLI) onboard the Global Change Observation Mission-Climate satellite as the primary input to provide stable terrestrial evapotranspiration maps with a 250 m spatial resolution. In this study, the algorithm was applied to a 4-year global dataset from 2018-2021 using a newly developed fully automated procedure. Accuracy assessment of the computational results was conducted using 12 ground-based flux monitoring datasets from AsiaFlux and AmeriFlux networks for 5 land-use categories (cropland, grassland, wetland, broadleaf forest, and needleleaf forest). The evaluation represented an affordable estimation accuracy with almost zero bias, with some random errors expressed as root-mean-squared error = 1.12 (mm day(-1)) and coefficient of variation = 0.51. Accuracy was higher for croplands and grasslands than for forests. The estimation accuracy of the algorithm was compared to that of the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16 global terrestrial evapotranspiration product. The ETindex algorithm showed a higher overall performance than MOD16, particularly for croplands and grasslands, which may be an advantage of the surface temperature-based approach used in the algorithm. Comparison with the global evapotranspiration product of the Global Land Evaporation Amsterdam Model (GLEAM) confirmed that the global spatial distribution and seasonal variations were consistent, except in some cold regions. The ETindex estimation algorithm and its derived ET products have considerable potential to advance terrestrial sustainability in natural environments, water management, and food production. They support this objective by providing robust and accurate evapotranspiration information.
The Agro-Meteorological Grid Square Data, NARO provides 1 km grid data on snow depth, snow water equivalent, and snowfall water equivalent estimated from general meteorological factors such as temperature and precipitation. For historical data, records are available from the 1980-1981 cold season onward. Although the data were first provided in 2017, the calculation algorithm was modified in 2024 to improve accuracy and reduce calculation time. Key updates include the direct use of AMGSDS humidity data, simplification of heat balance calculations, and the implementation of a latitude-dependent degree-day method for snowmelt forecasting. A major refinement was also made to the correction procedure using AMeDAS snow depth observations. Corrections are applied only when deviations exceed +/- 10%, and the adjustment ranges for both snowfall and snowmelt water equivalents were expanded to better capture natural variability. Using data from approximately 80 snow depth observation points installed by the Ministry of Land, Infrastructure, Transport, and Tourism, the accuracy of the calculation results from the old and new algorithms was compared for the three cold seasons of 2014-2015, 2015-2016, and 2016-2017. As a result, the estimation error (RMSE) of snow cover days decreased by 43%, and the estimation error of the snowmelt date decreased by 27%.
Measurement of internode elongation just below the shoot apex or growing point of the main stem is important for assessing plant growth. However, it is difficult to directly measure internode elongation on climbing plants with many leaves, such as cucumber plants. It is also difficult to measure the stem length of tall leafy plants in the field, and is prone to measurement errors. In addition, touching plants to measure them can stress them. Here, we measured internodal growth just below the shoot apex by using a 3D point cloud model reconstructed using Structure from Motion and Multi-View Stereo (SfM/MVS) methods under greenhouse conditions. The SfM/MVS method could nondestructively measure the internode elongation of multiple plants accurately and simultaneously with a root mean square error of 3.1 mm. Elongation was most active in the top two internodes and ceased in older internodes. Average elongation lengths of internodes 1 and 2 as counted from the top (6.7-7.7 mm day-1) were significantly greater than that of internode 3 (3.37 mm day-1), which was significantly greater than those of internodes 4 to 6 (0.0-0.5 mm day-1). These growth rates of two top internodes are the indicator of plant growth, which can be used for plant diagnosis. Our quantitative method for assessing internode elongation can be used under normal greenhouse conditions. Traditional 2D measurements face challenges due to occlusion, which this 3D method overcomes by digitally removing leaves for clear node visibility. 3D measurements enable time series analysis of internode elongation, which is difficult to measure in situ. The 3D data can be stored for later reanalysis.
The Food and Agricultural Organization (FAO) reported that agricultural sector absorbs the highest drought's direct impact, with multiple effect on food security and rural livelihoods. This study focuses on Indonesia to assess the long-term characteristics of agricultural drought hazards. In Indonesia, wet farming crops such as paddy are major agricultural commodities and staple foods, often planted up to three times per year. However, during the dry cropping season, these crops are highly susceptible to agricultural drought, posing a critical challenge to food security. This study utilized the Standardized Precipitation and Evapotranspiration Index (SPEI), specifically SPEI-3 to represent agricultural drought, to analyze multiple drought indicators-frequency, duration, and intensity-in Indonesia from 1981 to 2020. Monthly precipitation and potential evaporation data were obtained from the ERA5-Land dataset which provide precipitation and potential evapotranspiration with 0.1 degrees resolution from 1950. The assessment revealed an increasing trend in drought frequency, duration, and intensity over recent decades, particularly notable in South Sumatra and Java Island as the region with a high percentage of agricultural area. However, the most severe drought event occurred during 1991-2000, characterized by record-low precipitation compared to other decades. These findings are crucial for identifying hotspot regions to consider drought mitigation and preparedness strategies.
This study aimed to improve a procedure to determine an appropriate time scale and lag time to predict coffee yield at the local scale. The Vegetation Health Index (VHI), Effective Drought Index, Standardised Precipitation Index, Standardised Precipitation Evapotranspiration Index (SPEI), and soil moisture from 2000 to 2022 in Dak Lak, Vietnam, were selected for the analysis. The yield differences between drought and wet phases and the correlation coefficients between the indices and yield were analysed to determine potential predictors for the model. Then, a stepwise multiple linear regression model with the leave-one-out cross-validation was performed to select appropriate predictors with their time scales and lag times. The results showed that VHI with a lag time from seven to nine months before harvest (VHI7-9) and SPEI at a time scale of five months with a lag time of ten months before harvest (SPEI510) had essential contributions in predicting coffee yield. Meanwhile, soil moisture had a poor contribution. Coffee yield could be predicted from three to nine months before harvest based on meteorological drought indices, VHI, and soil moisture. With a reasonably long prediction time and relatively high accuracy, the proposed prediction procedures may be applied to climate for sustainable
The majority of recent land uses in drylands have been grasslands (32.2% of all drylands) and dryland forests (typical forest and sparse forest including shrub and savanna, 23%). If these lands are appropriately managed, they have the potential to sequester 0.84 Gt of soil organic carbon per year. However, climate change associated with global warming has led to an increase in temperature and irregular rainfall, which can both exacerbate the damage of droughts and desertification, and there is an urgent need to develop sustainable land management in arid regions. This study examined the interannual changes in the degraded land area in arid regions derived from a threshold value of the normalized difference vegetation index (NDVI) and the satellite-based aridity index (SbAI) from 2000 to 2023. Here, degraded land includes existing deserts and land having both permanent and temporal aeolian desertified areas. The total area of degraded lands was found to have exhibited a decreasing trend since 2000, but has gradually increased since about 2015. A method based on climo-vegetation regions is presented that uses NDVI and SbAI along with the climatological land use and classification of arid regions to understand current vegetation conditions as well as areas that deviated from their climatic potential.
Lettuce (Lactuca sativa L.) is a major vegetable cultivated worldwide and an essential vegetable consumed throughout the year in Japan. However, the suitable temperatures for open-field and tunnel-covered cultivation have not been sufficiently clarified. The present study aimed to comprehensively and quantitively clarify the suitable temperatures for the cultivation of head lettuce in major production areas across Japan using 1-km resolution daily mean air temperature and statistical data on cropping type, production, and planted area, planting date, and harvesting date on the prefectural and municipal levels. The suitable temperature for open-field cultivation was found to be in the inter-quantile range (IQR) of 13.9-20.5 degrees C. By contrast, the IQR in tunnel-covered cultivation, defined as the air temperature outside the tunnel, was estimated to be 4.3-9.8 degrees C. Furthermore, the growing period tended to be longer at lower temperatures. These findings contribute to our understanding of the cultivability for head lettuce and may inform projections of the effects of climate change on lettuce cultivation.
Methane (CH4) produced in rice paddy soil is transported to the atmosphere mainly via the rice plants and partly bubbling events (ebullition). Recent studies have shown that ebullition is more significant than previously thought fields planted with the popular Japanese cultivar 'Koshihikari'. It remains unclear whether the substantial contribution of ebullition is unique to this specific cultivar, as no previous reports have compared plant-derived and bubbling fluxes separately among various cultivars. Therefore, we planted 22 genetically diverse rice cultivars and measured plant-mediated and bubbling fluxes at three growth stages. Both fluxes, as well as the contribution of bubbling to the total flux, differed among the cultivars. The plant-mediated flux in Koshihikari was similar to or less than those other cultivars, whereas the bubbling flux and its contribution to total flux were larger, especially at the later stage. The absence of a correlation between plant-mediated flux and dissolved CH4 in the soil water at the later stage suggests that varietal differences in CH4 entry from the soil to the plant or gas flow permeability in the plant, rather than the pool size of CH4 in the soil, control the plant-mediated flux. On the other hand, the increase in bubbling flux associated with plant maturation and its close correspondence with dissolved CH4 concentration indicate that bubbling flux was controlled the size of CH4 pool in the soil, which likely increased with senescence and decay of rice roots. A low correspondence between panicle weight and CH4 emissions points to the potential for breeding high-yielding rice cultivars with low CH4 emissions
Extreme summer heatwaves in 2023 and 2024 severely disrupted the vegetable supply chain to the Tokyo metropolitan area. This study analyzes the impacts of these extreme climate events on urban food supply and market stability, using governmental statistics on wholesale arrival volumes and prices for fifteen major vegetables. Compared with the 2010–2022 baseline, the arrival volumes of carrot, Japanese radish, and tomato fell by 19–27%, while prices rose by 46–82%. The findings highlight how consecutive heatwaves amplified risks to food security and underscore the urgent need for climate-resilient agri-food systems. The study contributes to multidisciplinary research integrating agricultural meteorology, climate-impact assessment, and sustainable food-system management.
Volcanic ash soils have a high capacity to absorb CO2 because of their porous structure, which can lead to underestimation of soil respiration rates when measured using the static closed-chamber method. This study aimed to quantify the impact of CO2 adsorption on soil respiration measurements in volcanic ash soils using computational modeling based on adsorption data reported by Tabata et al. (2025). The Freundlich isotherm was used to calculate the adsorption ratio in two situations: (1) when the CO2 concentration was changed from 0 to 5% at any gas/soil ratio (volume/weight: v/w), and (2) for the gas/soil ratio at a certain concentration. Soil respiration rates in the simulated static closed-chamber method was estimated under conditions such as a vial as a container (volume of 136 mL) with a soil amount of 10 g per dry soil. The estimated soil respiration rates showed that the adsorption ratio at 5 degrees C reached 0.92 at a CO2 concentration of 5000 ppm and a gas/soil of 10. The amount of CO2 emitted by soil respiration was calculated under an interval of 600 seconds. The results showed that the adsorption ratio increased with increasing CO2 concentration or decreasing gas/soil ratio. This study highlights the importance of considering CO2 adsorption when measuring soil respiration in volcanic ash soils using the closed-chamber method, and provides insights for developing more accurate and reliable measurement methods.
