Assessing and predicting the challenges and uncertainties of rice cultivation due to climate change effects in Toyama Prefecture, Japan

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The resultant impact can be food insecurity. Japan's rice farms are in a precarious situation due to a multifaceted crisis driven by climate change. Utilizing remote sensing and the MaxEnt predictive modeling approach to identify declining rice fields can significantly enhance agricultural potential and provide a pertinent solution for integrated, place-based sustainable management in the regions. This technique suggests the likely abandonment of rice fields owing to climate change by identifying the principal factors contributing. The research aims to explore rice cultivation's scenarios, challenges, and uncertainties to bring policy-relevant place-based solutions for food security in Himi city, Toyama prefecture, Japan. The land-use and land-cover analysis shows that rice fields have decreased by 20.01 km² from 2000 to 2025 in Himi city. The results indicate that autumn and summer precipitation, along with mean minimum winter temperature, are the primary environmental factors influencing the potential abandonment distribution of rice fields, accounting for a cumulative contribution of > 63%. Rice fields are expected to decline northwestward, from 46.178km² to 26.17km². High probability for declined regions of rice field is predicted from > 3km² to > 7km². This decrease in rice fields and probable future decrease present potential risks for rice cultivation in the Himi city. Future efforts for mitigating the effects of climate change on rice planting adaptation should include sowing period changes, cultivar innovation, and rationalized application of fertilizer. These adaptable solutions are critical for advancing rural revitalization initiatives and promoting the integrated advancement of sustainable socio-environmental and agricultural development. This multi-disciplinary research seeks to contribute to Target 13 (climate action) of the Sustainable Development Goals (SDGs) and SDG 2, which aims to eradicate hunger, achieve food security, improve nutrition, and promote sustainable agriculture. Climate change land-use and land-cover Himi city MaxEnt model sustainable agriculture Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Rice is a staple food for more than half of the world's population and plays a vital role in global food security, particularly across Asia and parts of Africa, and Latin America (Fukagawa & Ziska, 2019 ). More than 20% of the world's calories come from it, and for hundreds of millions of smallholder farmers, it is their main source of income (Zeigler & Barclay, 2008 ). In addition to its nutritional value, rice farming promotes rural economies, employment, and cultural customs in a variety of geographical areas. Because of its importance, any interruption in rice production, whether due to resource shortages, market instability, or climate change, has a significant impact on local livelihoods as well as global food chains (FAO, 2004 ; Zeigler & Barclay, 2008 ) However, Japan's agriculture, culture, and food security have always been centered around rice. Rice cultivation, as the nation's primary crop and a symbol of identity, contributes uniquely to the preservation of traditional farming landscapes and rural lifestyles throughout the nation. The intricate relationship between rice cultivation and ecological management is shown in the traditional Satoyama landscapes, which are mosaic settings that blend rice paddies, woodlands, and human settlements (Takeuchi et al., 2003 ). However, a variety of complicated issues and uncertainties brought on by environmental change, global market dynamics, and socioeconomic shifts have impacted Japanese rice farming in recent decades. The agricultural workforce is getting smaller, and farmland abandonment is rising as a result of demographic shift, especially the aging and depopulation of rural areas (Yokoyama & Tanaka, 2019 ; Matsuda & Yamaguchi, 2020 ). The demographic shift in Japan is a significant source of uncertainty. The agricultural labor force has drastically decreased as a result of the nation's rapidly aging population and ongoing rural depopulation. Over 70% of Japanese farmers were over 65 as of 2020, and the country's full-time rice farmer population is declining yearly (MAFF, 2021). As younger generations move to cities and traditional family-based farming systems disappear, land abandonment makes this demographic catastrophe even worse (Matanle & Sato, 2010 ). Moreover, farmland fragmentation and inadequate utilization raise the possibility of ecological damage in addition to decreasing production efficiency (Kawasaki, 2010 ). Along with demographic and environmental concerns, economic uncertainty presents serious difficulties. Over the past 50 years, Japan's domestic rice consumption has gradually decreased as a result of shifting dietary customs and Westernized cuisine tastes. By 2020, per capita rice consumption had decreased from approximately 118 kg annually in 1962 to less than 50 kg annually (MAFF, 2021). The profitability of small-scale rice farming has been weakened by this drop, increased market rivalry, and the progressive elimination of government price subsidies as well as the liberalization of agricultural commerce (Kawasaki, 2010 ). Many farmers are consequently obliged to diversify into non-agricultural revenue streams or give up rice farming entirely due to their increasing financial instability. Simultaneously, rising temperatures, altered precipitation patterns, and a rise in extreme weather events are creating new layers of uncertainty due to climate change, which impact rice crop quality and yield stability. Planting times, water availability, pest prevalence, and eventually crop yields and quality are all directly impacted by these changes in rice farming (Iizumi, Hayashi & Kimura, 2007 ; Ishigooka et al., 2021 ). High temperatures during the grain-filling stage, for example, have been associated with a rise in chalky kernels, decreased grain weight and amylose content, and lower the quality and market value of rice (Du et al., 2023 ; Shimoyanagi et al., 2021 ). Adaptive management techniques and rice cultivars that are climate robust are becoming more and more necessary. Additionally, domestic rice consumption and market prices have been under pressure to decline due to trade liberalization and changing dietary habits, endangering the sustainability of small-scale producers. Aiming to increase productivity and sustainability, policy interventions have included the restructuring of Agricultural Cooperatives, the reduction of direct payments under the Rice Production Adjustment Program ( gentan ), and the promotion of "smart agriculture" technologies (Hirasawa, 2014 ; OECD, 2019 ). The efficacy of these tactics is still up for debate, though, especially when it comes to addressing older and smaller farm producers. Additionally, even though certain areas have embraced collaborative farming models and community-based efforts, scaling up such ideas presents governance and logistical issues. However, rice farming in Japan stands at the nexus of several overlapping uncertainties, including institutional, economic, environmental, climatic, and demographic issues. The future of Japanese agriculture, as well as more general concerns about food security, rural resilience, and sustainable land use in aging societies, depends on an understanding of the intricate dynamics of these issues. The standing of Japan's rice cultivation system and its ability to adjust to socio-ecological and climatic disruptions must be critically examined in light of these intertwining constraints. Researchers are interested in this issue, seeking appropriate methods to mitigate this situation and improve rice varieties to enhance food security. This research is pioneering both academically and socially by utilizing remote sensing technology to detect declining paddy fields and identify high-risk hotspot areas for necessary action. In order to maintain this vital industry, this paper examines the various challenges and uncertainty issues that Japanese rice farmers face, looks into the causes of uncertainty, and evaluates community- and place-based solutions. The current status of rice farming in Japan is examined in this paper by comparing the changes in rice fields in the years 2000 and 2025, along with the main causes and factors of challenges and uncertainty, and the ability of institutions, communities, and farmers to adjust to the fast-paced socio-ecological change. The main goal of this research is to detect changes in rice fields, identify abandoned farmland, declining paddy fields, cultivation challenges, and uncertainties, thereby providing location-based solutions relevant to food security policies. The specific objectives of this research are: 1) mapping and differentiating the past and present stands, losses, and changes of agricultural land use in the study sites; 2) predicting probable hotspot areas of rice field decline and identifying the responsible factors/drivers of rice field decline in the context of climatic changes; 3) endorsing place-based solutions for sustainable adaptation based on the predictive modeling approach findings. This research investigates the meteorological factors to predict challenges facing future rice production in Toyama, Japan. The use of predictive modeling and remote sensing technology is a novel approach in this field. This interdisciplinary research aims to contribute to SDGs 13 and 2 (climate action and ending hunger, achieving food security, and promoting sustainable agriculture). The findings support policymakers, local communities, and other experts, contributing to Japan's revitalization by providing place-based, policy-relevant solutions. 2. Data and Methods 2.1 Study area This research is based on an integrated assessment of Himi City in Toyama Prefecture, Japan, and aims to support data-driven decision-making for sustainable and effective crop management. Himi (氷見市, Himi-shi ) is a city in western Toyama Prefecture, Japan, located at 36°51′24″N and 136°58′23″E (Fig. 1 ). This city had an estimated population of 48,275 in 17,632 households and a population density of 210 persons per km². Its total area is 230.56 km² (89.02 sq mi) (Himi City, 2020 ; Statistics Bureau Japan, 2020). Himi is famous primarily for its commercial fishing industry. The city was established on August 1, 1952. Himi is located in Toyama Prefecture's extreme northwest and is bounded to the east by Toyama Bay in the Sea of Japan and to the west and north by Ishikawa Prefecture, which includes the Noto Peninsula. A large portion of the territory is made up of scattered settlements, which are common in this part of Japan. Himi has a humid continental climate ( Köppen Cfa ) characterized by warm summers and cold winters with plenty of snowfall. The average annual temperature in Himi is 13.9°C, and rainfall is 2409 mm, with September as the wettest month. The temperatures are highest on average in August, at roughly 26.4°C, and lowest in January, at around 2.8°C (Climate data, 2025 ). 2.2 Data collection, processing, and accuracy assessment In this study, satellite data of Landsat 5 and 9 were collected from the USGS Earth Explorer ( https://earthexplorer.usgs.gov/ ), a web-based platform for accessing spatial data. Landsat 5 imagery from the year 2000 and Landsat 9 imagery from 2025 were selected for spatial analysis of land use and land cover (LULC). Both images were acquired in April to maintain seasonal consistency. Initially, the TIFF band files were processed through band composition, followed by supervised classification using a Support Vector Machine (SVM) algorithm using the Maximum Likelihood Classification (MLC) method. The classified raster data was then converted to vector format (polygon) and dissolved to merge adjacent features with similar attributes. Finally, the data were clipped to the study area boundary, and the area of each land cover class was calculated. To ensure high thematic accuracy, 150 representative training samples were collected for each land cover class. Quantitative analysis was then conducted within the GIS environment to calculate the area of each class in square kilometers. The decline in rice fields from 2000 to 2025 was detected. ArcGIS Pro software (version: ArcGIS Pro 3.4.0) and QGIS (QGIS 3.34.12) software were used to process and analyze the data. Ground truthing was conducted by field survey, and the accuracy of the analysis was assessed using the kappa coefficient . For the accuracy assessment, a random point sampling technique was applied for both the years 2000 and 2025. A total of 40 validation samples were selected for each LULC class, resulting in 240 reference points. These points were converted into KML format and overlaid on high-resolution imagery in Google Earth Pro for visual interpretation and reference data verification. The geographical coordinates of the abandoned rice fields were obtained through a field survey and a Google Earth survey. The abandoned rice fields were identified by comparing the satellite data of 2000 and 2025. The future scenarios, challenges, and uncertainties of rice cultivation were predicted using the Maximum Entropy model (Phillips et al., 2006 ). The seasonal climate variables (38 variables) were derived from ClimateAP v3.10 across the new emissions scenario from CMIP6, called “Shared Socioeconomic Pathways” (SSPs) of access-esm-SSP5-8.5 (Wang et al., 2017 ). These data were extracted, downscaled, and gridded (4 ✕ 4 km) as monthly climate data for the reference from PRISM (Daly et al., 2008 ) and WorldClim (Hijmans et al., 2005 ). The future climate projections were selected from the General Circulation Models (GCMs) of the Coupled Model Intercomparison Project (CMIP6) included in the IPCC Sixth Assessment Report (AR6) for 2011–2100. 2.3 These Seasonal variables Among 38 seasonal climate variables, i.e., mean temperature (°C), mean maximum temperature (°C), mean minimum temperature (°C), and precipitation (mm) in winter (December (from the previous year for an individual year), January and February), spring (March, April and May), summer (June, July and August), and autumn (September, October and November) was used as directly calculated variables from the scenario of access-esm-SSP5-8.5. The other derived seasonal climate variables are degree-days below 0°C, degree-days above 5°C, and degree-days below 18°C, for winter (December (from the previous year for an individual year), January and February), spring (March, April and May), summer (June, July and August), and autumn (September, October and November) including the number of frost-free days (NFFD) and precipitation as snow (PAS) (mm) between August in the previous year and July in current year (Wang et al., 2017 ). Strong correlations among environmental variables may cause multicollinearity concerns, resulting in greater variation in parameter estimations and probable overfitting of the MaxEnt model findings (Xinyi et al., 2025 ; Halvorsen et al., 2016 ). Therefore, we calculated the correlations of all environmental variables using Pairwise Pearson correlation (Wang et al., 2024 ) to identify and remove highly collinear variables ( r > 0.8, 0.85, or 0.9). First, all environmental variables were included in the MaxEnt model to determine variable importance using the jackknife test. If the correlation coefficient between two variables is ≧ 0.8 (Mason and Perreault, 1991 ), the variable with the highest contribution rate was kept for further model analyses. Finally, after examining the above steps, eight environmental variables were converted to the ASCII raster grid format using ArcGIS Pro software. All this eight variables were used for MaxEnt modeling to model the distribution of probable rice field decline areas. 2.4 MaxEnt modeling algorithm In this study, MaxEnt (version 3.3.3) modeling was conducted to show the probable areas of rice field abandonment and to investigate the relationship between spatial distribution and the climatic variables. Twenty-five percent of the collected data was randomly selected as testing data, and the remaining seventy-five percent was for training data (Guerra-Coss et al. 2021 ; Shabani et al. 2020 ; Liu et al., 2019 ). The model was performed with 5 replicates to ensure its stability (Wang et al., 2022 ). The MaxEnt software was used to calculate the testing set's area under the curve (AUC), which ranged from 0 to 1. An AUC value of 0.5 or less denoted poor performance, but an AUC value around 1 suggested flawless prediction (Wang et al. 2021b ). The model's performance was assessed using the receiver operating characteristic (ROC) (Phillips et al., 2006 ; Manzoor et al., 2021 ). The percentage contribution of the variables was assessed in order to investigate the significance of the variables in locating the probable declined rice field distribution locations. Additionally, the relationship between the variables and probability distribution was demonstrated using response curves. The software's built-in jackknife test was used to calculate each variable's contribution to the potential distribution area (Liu et al., 2019 ; Hill et al., 2012 ). How much advantage is obtained from each variable alone or from all the factors together is reflected in the built-in jackknife test findings. A higher gain value for a single variable means that the variable contains more information or contributes to the waste distribution area (Wang et al., 2017 ). The contribution of climatic factors of declination was identified via the Jackknife test of the modeling. The high-risk "hot spots" area of rice cultivation was shown in the maps of results section, which will support the policymakers, professionals, or locals to concentrate on further revival of the assessed study sites. The findings of this research will recommend possible suitable scenarios and responsible climatic parameters for rice cultivation in the future. 3. Results 3.1 Land use land cover (LULC) change detection in Himi city LULC classification for Himi, Toyama, was accomplished to evaluate environmental changes between 2000 and 2025. The analysis utilized multi-temporal satellite data, specifically Landsat 5 TM for the year 2000 and Landsat 9 OLI-2 for the 2025 assessment. Figure 2 illustrates the LULC maps of Himi city for 2000 and 2025. This map shows the comparison of the land types in the study site. The red colored area represents the built-up area, while the yellow-colored area represents the rice field area in Himi city. The green area stands for vegetation cover, and the blue area is for water bodies. The overall area of Himi city was 230.56 km², from where the built-up area increased to 0.47 km², and vegetation cover increased by 20.06 km² from 2000 to 2025 (Table 1 ). Some agricultural land or rice fields were converted into plantations and settlements. Also, agricultural land was abandoned in the area, which was converted into an orchard or natural vegetation cover. On the other hand, water bodies have decreased by 0.15 km², and rice fields have decreased by 20.01 km² because of the abandonment of agricultural fields and conversion to orchards. Table 1 LULC classification table and area statistics of Himi city, Toyama, Japan LULC Class (Year 2000) (Year 2025) Gain/Loss Area in Sq.km. 1 Built-up area 40.668 41.142 + 0.47 2 Water bodies 0.578 0.438 -0.15 3 Vegetation Cover 143.417 163.480 + 20.1 4 Rice Cultivation 46.178 26.173 -20.01 The following 15 local key areas were selected to predict the future trends of rice fields based on the substantial rice filed abandonment, i.e., 新保 (Shinbo), 論田 (Ron-den), 小窪 (Okubo), 田江 (Tae), 早借 (Hayakashi), 小久米 (Ogume), 日詰 (Hizume), 日名田 (Hinata), 三尾 (Mio), 床鍋 (Tokonabe), 葛葉 (Kuzuba), 久目 (Kume), 触坂 (Furezaka), 岩瀬下岩瀬 (Iwase Shimoiwase ), 見内 (Miuchi). The LULC classification analysis was conducted before conducting MaxEnt modeling in these 15 local areas (Fig. 3 ). The comparative results reveal significant shifts in the landscape. Most notably, rice field/agriculture areas showed a substantial decline, dropping from 5.42 sq. km to 3.11 sq. km from 2000 to 2025 (Table 2 ). Conversely, bare land experienced a sharp increase, growing from 0.08 sq. km to 0.61 sq. km, suggesting potential land abandonment or preparation for development activities. While evergreen forests saw a minor reduction, deciduous forest areas expanded significantly from 11.05 sq. km to 13.05 sq. km, indicating a notable trend of reforestation or natural succession in the region over the 25 years. Table 2 LULC classification table and area statistics of 15 local areas in Himi city, Toyama, Japan Class name Area in 2000 (Sq.km) Area in 2025 (Sq.km) Waterbodies 1.466 1.307 Bare land 0.076 0.614 Deciduous forest 11.049 13.054 Homestead vegetation 3.725 3.827 Rice field/agriculture 5.419 3.105 Evergreen forest 6.938 6.767 Note : 新保 (Shinbo), 論田 (Ron-den), 小窪 (Okubo), 田江 (Tae), 早借 (Hayakashi), 小久米 (Ogume), 日詰 (Hizume), 日名田 (Hinata), 三尾 (Mio), 床鍋 (Tokonabe), 葛葉 (Kuzuba), 久目 (Kume), 触坂 (Furezaka), 岩瀬下岩瀬 (Iwase Shimoiwase ), 見内 (Miuchi) areas Based on the resulting confusion matrix of the Kappa coefficient , the classification achieved an overall accuracy of 82.92% in 2000, with a к coefficient of 0.80, indicating strong agreement between the classified map and reference data. In 2025, the accuracy assessment results showed an overall accuracy of 86.67% and a к coefficient of 0.84, reflecting an improved classification accuracy. 3.2 Prediction of probable rice field scenario using Maxent modeling The Maxent model output shows an enlarged area of rice field decline or abandonment than the existing area. The output range of the modeling result varied from 0 to 1. The value near zero means lower susceptibility, and the value near one means higher susceptibility. The warmer color shows the area of higher vulnerability to abandonment of the rice field. The average AUC is 0.893, which represents the higher accuracy of the model. Figure 4 shows the prediction scenario of rice field abandonment in Himi city using Maxent modeling. The predicted area was classified into four classes. The lower susceptible area ranged up to 0.19, the medium susceptible area ranged from 0.193 to 0.38, the medium-high area ranged from 0.39 to 0.57, and the highly susceptible area ranged from 0.58 to 0.88. The high vulnerability areas are red in color, and the low vulnerability areas are blue in color. Figure 4 showed that most of the north-western parts of the area are at risk of abandonment. The highly vulnerable areas that fall under the great risk of probable abandonment are Ron-den, Mio, Tokonabe, and Hinata, which comprise an area of 7.40 km². The medium and medium-high areas of probable threat to decline include Ogume, Kume, and most of Miuchi area, comprising 10.7 km². On the other hand, the less vulnerable area consists of Shinbo, Tae, Hayakashi, part of Iwase Shimoiwase, and the Furezaka area. The total vulnerability area to decline comprised 18.1 km². 3.3 Contribution of climatic variables and their response curves to the model The following table (Table 3 ) gives estimates of the relative contributions of the environmental variables to the Maxent model. To calculate the first estimate, each iteration of the training algorithm adds the increase in regularized gain to the contribution of the relevant variable. For the second estimate, the values of each environmental variable on training and background data are randomly permuted. The model is reevaluated using the permuted data, and the resulting decline in training AUC is displayed in the table, normalized to percentages. Values shown are averages over replicate runs. The highest cumulative contributing variables were precipitation in autumn and summer, combinedly contributing > 50%. The third contributing variable is the mean minimum temperature in winter is > 12%, and the fourth one is precipitation as snow > 12%. The number of frost-free days in spring (nffd) has the highest permutation importance of 25.2. The second and third permutation importance was for the variables of autumn and summer precipitation of 23.4 and 18.9, respectively. The fluctuation or changes of these variables may cause changes or decline in rice fields in the future years. Table 3 Relative contributions of the environmental variables to the Maxent model Variables Percent contribution Permutation importance Precipitation for autumn (mm) 31.9 23.4 Precipitation for summer (mm) 18.9 18.9 Mean minimum temperature (°C) for winter 12.4 11.7 Precipitation as snow between August in the previous year and July in the current year (mm) 12.2 3.5 Number of frost-free days for spring 9.1 25.2 Mean maximum temperature (°C) for summer 8.8 10.4 Precipitation for spring (mm) 5.9 4.7 Degree-days below 0°C for spring 0.8 2.3 The response curves in Fig. 5 show how each environmental variable influences the Maxent prediction. The curves depict how the anticipated probability of presence changes as each environmental variable is modified, while all other environmental variables remain at their average sample value. The curves depict the mean response of the five replicate Maxent runs (red) and the mean +/- one standard deviation (blue, with two shades for categorical variables). These plots also show the expected suitability's dependence on the selected variable, as well as the dependencies caused by correlations between the selected variable and others. Precipitation of autumn has the highest influences on the model when the value is 635 to 639 mm. Again, precipitation of summer has the highest influences on the model when the value is 584 to 596 mm with some fluctuations. The mean minimum temperature in winter impacts the model output when the range is 1.0 to 1.8°C, with fluctuations inside the graph. When the number of frost-free days for spring is lower, i.e., 81, the impact is higher, and it reduces with the increase in the number of days. Degree-days below 0°C for spring, precipitation as snow, and Mean maximum temperature (°C) for summer has similar concave trend of effects on the model. The findings of the jackknife test of variable relevance are displayed in the following Fig. 6 . The mean minimum temperature for winter appears to provide the most useful data when utilized alone, as it is the environmental variable with the biggest gain. Precipitation during autumn appears to have the greatest information that isn't present in the other variables because it is the environmental variable that reduces the gain the most when it is removed. The values displayed are averages across multiple runs. 4. Discussion 4.1 Decline of rice fields and the responsible climatic factors Significant changes in the landscape have occurred between 2000 and 2025, according to comparative land-use and land-cover studies. Most significantly, rice fields and agricultural areas decreased abruptly from 64 km² to 26 km², indicating widespread agricultural abandonment linked to depopulation, aging farming populations, and diminished economic viability of small-scale rice cultivation in Japan (MAFF, 2021; Kobayashi et al., 2020 ). Conversely, the area of vegetation cover expanded from 143 km² to 163 km², signifying transitory land conditions sometimes associated with land abandonment, postponed redevelopment, or colonization of vegetation species (Seto et al., 2012 ). Forest cover dynamics further reveal ecological succession processes. Deciduous woods significantly increased while evergreen forest areas slightly decreased. This trend is consistent with secondary forest regeneration following agricultural abandonment, where deciduous species dominate early- to mid-successional stages in rural Japan (Takeuchi, 2010 ; Kobayashi et al., 2020 ). Employing the MaxEnt modeling approach, this study identified key climatic variables influencing the spatial distribution and persistence of rice fields. MaxEnt is especially adept for this investigation as it can estimate probabilities utilizing presence-only data and intricate, non-linear correlations between rice occurrence and environmental factors. Model results indicated that variables related to precipitation patterns (e.g., precipitation of autumn and summer) are the most influential, reflecting rice’s strong dependence on consistent water availability. Temperature-related factors, notably mean minimum temperature in the winter season, correspondingly showed an adverse effect by fostering evapotranspiration and heat stress, which reduce yields and inhibit continued cultivation. Inadequate or inconsistent summer and autumn precipitation restricts water availability throughout critical growth and harvesting phases, heightening drought stress and diminishing yields, whereas excessive rainfall can harm crops and postpone harvests (IRRI, 2013; Yoshida, 1981 ). Increasing average minimum winter temperatures, a significant indicator of global warming, diminish snow accumulation in temperate regions such as Japan, hence undermining snowmelt-dependent irrigation systems crucial for spring paddy flooding (Atmowidjojo et al., 2024 ). Moreover, an augmentation in frost-free days modifies rice phenology, hastens growth cycles, and may elevate pest and disease pressure, thus lowering long-term field viability (Kondo et al., 2000 ). Collectively, these climatic changes lead to decreasing rice field viability and land abandonment. 4.2 Implications for place-based solutions for sustainable adaptation The regional variability of environmental factors impacting land-use change is highlighted by the MaxEnt modeling results, underscoring the necessity of place-based adaptation measures. Areas with high adaptability driven by climatic variables such as seasonal precipitation, winter minimum temperature, and frost-free days indicate locations where conventional land uses, including rice agriculture, remain viable under changing climate circumstances (Phillips et al., 2006 ; Elith et al., 2010 ). In these high-probability zones of decline, targeted assistance for climate-resilient farming methods can increase adaptive capacity and minimize agricultural land abandonment. In general, MaxEnt-informed place-based solutions enhance long-term sustainability by aligning adaptation strategies to local social and environmental conditions. 5. Conclusion The high-risk "hot spots" areas of rice field abandonment are stated as medium-high and high vulnerability. These findings will support policymakers, professionals, or locals to concentrate and take measures on further revival of the rice-cultivated area in the assessed study sites. The findings of this research recommend that specialists and government bodies focus on the protection of rice fields abandonment based on the predicted scenarios of vulnerability in rice fields. Further research is required to predict the future trend based on the socio-economic conditions. Overall, the MaxEnt-based results showed that sustainable land-use planning requires place-based, climate-informed adaptation techniques. Decision-makers can improve resilience, protect biodiversity, and minimize maladaptive land-use change under persistent climatic pressure by coordinating development constraints, ecological restoration, and agricultural support with locally dominating environmental factors. Declarations Consent for publication Not applicable. Funding Declaration This work was supported by the Public Interest Incorporated Foundation ‘Toyama Daiichi Bank Scholarship Foundation’. This foundation had no role in study design; data collection, analysis, and interpretation; the writing of the report; or the decision to submit the article for publication. Availability of data and materials The datasets generated and/or analysed during the current study are available from https://climateap.net/, https://web.climateap.net, and https://worldclim.org/ Author Contribution Declaration Sharmin Shishir wrote the main manuscript text, conducted the MaxEnt modeling using climate change scenarios, and prepared figures 1, 4, 5, and 6. Naiem Mollah performed the spatial analysis of land use and land cover change and prepared the figures 2 and 3. All authors reviewed the manuscript. Ethics approval and consent to participate Not applicable. Competing Interest The authors declare that they have no competing interests. Acknowledgments We acknowledge the Public Interest Incorporated Foundation ‘Toyama Daiichi Bank Scholarship Foundation’ for funding this research. We thank Mr. Wikas Younis for supporting the administrative data collection. We are also grateful to Haque Md Ariful and Md Masuk Mowla Aunkur for their assistance during the statistical analysis. References Atmowidjojo AC, Huang CK, Arima Y (2024) Japan Initiatives of Climate Change Adaptation for Paddy Rice Commodity. 1–12. http://hdl.handle.net/2433/286777 Climate data (2025) Data and graphs for weather & climate in Himi. Date accessed: October 7th, 2025, 15:08 pm. URL: https://en.climate-data.org/asia/japan/toyama/himi-5536/ Daly C, Halbleib M, Smith JI, Gibson WP, Doggett MK, Taylor GH, Curtis J (2008) Physiographically sensitive mapping of temperature and precipitation across the conterminous United States. Int J Climatol 28:2031–2064 Du Y, Long C, Deng X, Zhang Z, Liu J, Xu Y, Liu D, Zeng Y (2023) Physiological Basis of High Nighttime Temperature–Induced Chalkiness Formation during Early Grain–Filling Stage in Rice (Oryza sativa L). 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Australian J Agricultural Resource Econ 54(4):509–526. https://doi.org/10.1111/j.1467-8489.2010.00509.x Kobayashi Y, Higa M, Higashiyama K, Nakamura F (2020) Drivers of land-use changes in societies with decreasing populations: A comparison of the factors affecting farmland abandonment in a food production area in Japan. PLoS ONE 15(7):e0235846. https://doi.org/10.1371/journal.pone.0235846 Kondo M, Murty MVR, Aragones DV (2000) Characteristics of root growth and water uptake from soil in upland rice and maize under water stress. Soil Sci Plant Nutr 46(3):721–732 Liu Y, Huang P, Lin F, Yang W, Gaisberger H, Christopher K, Zheng Y (2019) MaxEnt modelling for predicting the potential distribution of a near threatened rosewood species (Dalbergia cultrata Graham ex Benth). 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Ecol Ind, 120 Mason CH, Perreault WD (1991) Collinearity, power, and interpretation of multiple regression analysis. J Mark Res 28(3):268–280. 10.2307/3172863 Matanle P, Sato Y (2010) Coming soon to a city near you! Learning to live 'beyond growth' in Japan's shrinking regions. Social Science Japan J 13(2):187–210 Matsuda Y, Yamaguchi K (2020) Does the downward trend in agricultural land use continue in depopulated areas? Evidence from the Tohoku Region, Japan. Int J Environ Rural Dev 11(1):187–193. https://www.jstage.jst.go.jp/article/ijerd/11/1/11_187/_article/-char/ja Ministry of Agriculture, Forestry and Fisheries (MAFF) (2021) Annual Report on Food, Agriculture and Rural Areas in Japan. Date accessed: October 6 th, 2025, 2:32 pm. URL: OECD (2019) Innovation, Agricultural Productivity and Sustainability in Japan. OECD Food and Agricultural Reviews. Date accessed: October 7 th, 2025, 10:36 pm. URL: https://www.oecd.org/en/publications/innovation-agricultural-productivity-and-sustainability-in-japan_92b8dff7-en.html? Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190(3–4):231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 Seto KC, Güneralp B, Hutyra LR (2012) Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Sustain Sci 109(40):16083–16088. https://doi.org/10.1073/pnas.1211658109 Shabani F, Ahmadi M, Kumar L, Solhjouy-fard S, Tehrany MS, Shabani F, Kalantar B, Esmaeili A (2020) Invasive weed species’ threats to global biodiversity: future scenarios of changes in the number of invasive species in a changing climate. Ecol Ind 116:10 Shimoyanagi R, Abo M, Shiotsu F (2021) Higher Temperatures during Grain Filling Affect Grain Chalkiness and Rice Nutrient Contents. Agronomy 11(7):1360. https://doi.org/10.3390/agronomy11071360 Statistics BJ (2020) Toyama (Japan): Cities, Towns, and villages in prefecture. Date accessed: October 7th, 2025, 15:15 pm. URL: https://www.citypopulation.de/en/japan/toyama/ Takeuchi K (2010) Rebuilding the relationship between people and nature: the Satoyama Initiative. Ecol Res 25:891–897. https://doi.org/10.1007/s11284-010-0745-8 Takeuchi K, Brown RD, Washitani I et al (2003) Satoyama: The Traditional Rural Landscape of Japan . 1st edition. Tokyo, Springer. https://doi.org/10.1007/978-4-431-67861-8 Wang F, Yuan X, Sun Y, Liu Y (2024) Species distribution modeling based on MaxEnt to inform biodiversity conservation in the Central Urban Area of Chongqing Municipality. Ecol Ind 158. https://doi.org/10.1016/j.ecolind.2023.111491 Wang M, Chen H, Lei M (2022) Identifying potentially contaminated areas with MaxEnt model for petrochemical industry in China. Environ Sci Pollut Res 29:54421–54431. https://doi.org/10.1007/s11356-022-19697-8 Wang RL, Li Q, Feng CH, Shi ZP (2017) Predicting potential ecological distribution of Locusta migratoria tibetensis in China using MaxEnt ecological niche modeling. Acta Ecol Sin 37(24):8556–8566 Wang T, Wang G, Innes JL, Seely B, Chen B (2017) ClimateAP: an application for dynamic local downscaling of historical and future climate data in Asia Pacific. Front Agr Sci Eng. 10.15302/j-fase-2017172 Wang Y, Chao B, Dong P, Zhang D, Yu W, Hu W, Ma Z, Chen G, Liu Z, Chen B (2021b) Simulating spatial change of mangrove habitat under the impact of coastal land use: coupling MaxEnt and Dyna-CLUE models. Sci Total Environ 788:147914–147914 Xinyi F, Zhixin H, Quansheng G (2025) Climate change reshapes rice geographical indications in China: Opportunities and challenges of rural revitalization. J Clean Prod 529:0959–6526. https://doi.org/10.1016/j.jclepro.2025.146760 Yokoyama S, Tanaka K (2019) Spatial pattern of farmland abandonment in Japan: Identification and determinants. Sustainability 10(10):3676. https://doi.org/10.3390/su10103676 Yoshida S (1981) Fundamentals of Rice Crop Science. IRRI. http://books.irri.org/9711040522_content.pdf Zeigler RS, Barclay A (2008) The Relevance of Rice. Rice 1:3–10. https://doi.org/10.1007/s12284-008-9001-z Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Apr, 2026 Read the published version in Environmental Systems Research → Version 1 posted Editorial decision: Revision requested 23 Feb, 2026 Reviews received at journal 18 Feb, 2026 Reviews received at journal 07 Feb, 2026 Reviewers agreed at journal 07 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviewers invited by journal 04 Feb, 2026 Editor assigned by journal 04 Feb, 2026 Submission checks completed at journal 04 Feb, 2026 First submitted to journal 29 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8736594","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587461891,"identity":"ffc100bd-cafc-4a4f-9d2b-af455afa2014","order_by":0,"name":"Sharmin Shishir","email":"data:image/png;base64,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","orcid":"","institution":"University of Toyama","correspondingAuthor":true,"prefix":"","firstName":"Sharmin","middleName":"","lastName":"Shishir","suffix":""},{"id":587461893,"identity":"68440df1-cb56-45f5-a2ee-aa0c38d71926","order_by":1,"name":"Naiem Mollah","email":"","orcid":"","institution":"University of Toyama","correspondingAuthor":false,"prefix":"","firstName":"Naiem","middleName":"","lastName":"Mollah","suffix":""}],"badges":[],"createdAt":"2026-01-30 03:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8736594/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8736594/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40068-026-00474-2","type":"published","date":"2026-04-05T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102228907,"identity":"09566a26-fc19-40af-82df-e02143333b15","added_by":"auto","created_at":"2026-02-09 15:11:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":324619,"visible":true,"origin":"","legend":"\u003cp\u003ea. Location of Toyama prefecture in Japan by red color, b. Himi city (black border) in Toyama prefecture, c. the selected study areas in Himi city are shown in red color.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8736594/v1/b6633c8a937b183fc70f06c0.png"},{"id":102228904,"identity":"6c7b6c0c-fb29-4ba3-b5c5-457904cc1b35","added_by":"auto","created_at":"2026-02-09 15:11:16","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":340601,"visible":true,"origin":"","legend":"\u003cp\u003eLULC classification map of Himi city, Toyama, Japan. The left side map represents the year 2000, and the right side represents the year 2025. Yellow color is for rice fields, green color for vegetation cover, blue color for water bodies, and red color for built-up areas.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8736594/v1/00059e0b52dd3ddc1758dff0.jpeg"},{"id":102228980,"identity":"3af12a02-014d-40cf-a6e9-67b34e620ffc","added_by":"auto","created_at":"2026-02-09 15:11:51","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":112670,"visible":true,"origin":"","legend":"\u003cp\u003eSelected area of Himi city for the prediction modeling shown in the inset map. The maps show the LULC of 6 classes in 2000 and 2025.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8736594/v1/aae3b902c43d9a3128c1610e.jpeg"},{"id":102228864,"identity":"2f9769a4-c16a-4f5c-9080-febaf1f5ea52","added_by":"auto","created_at":"2026-02-09 15:11:09","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":172531,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction scenario of rice field abandonment in Himi city using Maxent modeling\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8736594/v1/cb4f0024e6a33f653fe485f1.jpeg"},{"id":102228809,"identity":"d62e1a11-01d4-4ec7-904a-308d6ced50e2","added_by":"auto","created_at":"2026-02-09 15:10:54","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":211029,"visible":true,"origin":"","legend":"\u003cp\u003eResponse curve of the eight variables in Maxent modeling\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8736594/v1/d96283e96190c5b5b359108b.jpeg"},{"id":102228952,"identity":"b8895186-fe0f-46fb-be39-d54b1dfde4ab","added_by":"auto","created_at":"2026-02-09 15:11:36","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":122960,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of the jackknife test of variable importance by maxent model\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8736594/v1/7731d0f93380b07e566e3b55.jpeg"},{"id":106343303,"identity":"41094764-56a4-4bc5-8e7e-ee43505f4ef0","added_by":"auto","created_at":"2026-04-07 16:01:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2082750,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8736594/v1/7dc991fe-4af1-4588-b4bb-22ecc67feefb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing and predicting the challenges and uncertainties of rice cultivation due to climate change effects in Toyama Prefecture, Japan","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRice is a staple food for more than half of the world's population and plays a vital role in global food security, particularly across Asia and parts of Africa, and Latin America (Fukagawa \u0026amp; Ziska, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). More than 20% of the world's calories come from it, and for hundreds of millions of smallholder farmers, it is their main source of income (Zeigler \u0026amp; Barclay, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In addition to its nutritional value, rice farming promotes rural economies, employment, and cultural customs in a variety of geographical areas. Because of its importance, any interruption in rice production, whether due to resource shortages, market instability, or climate change, has a significant impact on local livelihoods as well as global food chains (FAO, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Zeigler \u0026amp; Barclay, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eHowever, Japan's agriculture, culture, and food security have always been centered around rice. Rice cultivation, as the nation's primary crop and a symbol of identity, contributes uniquely to the preservation of traditional farming landscapes and rural lifestyles throughout the nation. The intricate relationship between rice cultivation and ecological management is shown in the traditional \u003cem\u003eSatoyama\u003c/em\u003e landscapes, which are mosaic settings that blend rice paddies, woodlands, and human settlements (Takeuchi et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, a variety of complicated issues and uncertainties brought on by environmental change, global market dynamics, and socioeconomic shifts have impacted Japanese rice farming in recent decades. The agricultural workforce is getting smaller, and farmland abandonment is rising as a result of demographic shift, especially the aging and depopulation of rural areas (Yokoyama \u0026amp; Tanaka, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Matsuda \u0026amp; Yamaguchi, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The demographic shift in Japan is a significant source of uncertainty. The agricultural labor force has drastically decreased as a result of the nation's rapidly aging population and ongoing rural depopulation. Over 70% of Japanese farmers were over 65 as of 2020, and the country's full-time rice farmer population is declining yearly (MAFF, 2021). As younger generations move to cities and traditional family-based farming systems disappear, land abandonment makes this demographic catastrophe even worse (Matanle \u0026amp; Sato, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Moreover, farmland fragmentation and inadequate utilization raise the possibility of ecological damage in addition to decreasing production efficiency (Kawasaki, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Along with demographic and environmental concerns, economic uncertainty presents serious difficulties. Over the past 50 years, Japan's domestic rice consumption has gradually decreased as a result of shifting dietary customs and Westernized cuisine tastes. By 2020, per capita rice consumption had decreased from approximately 118 kg annually in 1962 to less than 50 kg annually (MAFF, 2021). The profitability of small-scale rice farming has been weakened by this drop, increased market rivalry, and the progressive elimination of government price subsidies as well as the liberalization of agricultural commerce (Kawasaki, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Many farmers are consequently obliged to diversify into non-agricultural revenue streams or give up rice farming entirely due to their increasing financial instability.\u003c/p\u003e \u003cp\u003eSimultaneously, rising temperatures, altered precipitation patterns, and a rise in extreme weather events are creating new layers of uncertainty due to climate change, which impact rice crop quality and yield stability. Planting times, water availability, pest prevalence, and eventually crop yields and quality are all directly impacted by these changes in rice farming (Iizumi, Hayashi \u0026amp; Kimura, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ishigooka et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). High temperatures during the grain-filling stage, for example, have been associated with a rise in chalky kernels, decreased grain weight and amylose content, and lower the quality and market value of rice (Du et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shimoyanagi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Adaptive management techniques and rice cultivars that are climate robust are becoming more and more necessary.\u003c/p\u003e \u003cp\u003eAdditionally, domestic rice consumption and market prices have been under pressure to decline due to trade liberalization and changing dietary habits, endangering the sustainability of small-scale producers. Aiming to increase productivity and sustainability, policy interventions have included the restructuring of Agricultural Cooperatives, the reduction of direct payments under the Rice Production Adjustment Program (\u003cem\u003egentan\u003c/em\u003e), and the promotion of \"smart agriculture\" technologies (Hirasawa, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; OECD, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The efficacy of these tactics is still up for debate, though, especially when it comes to addressing older and smaller farm producers. Additionally, even though certain areas have embraced collaborative farming models and community-based efforts, scaling up such ideas presents governance and logistical issues.\u003c/p\u003e \u003cp\u003eHowever, rice farming in Japan stands at the nexus of several overlapping uncertainties, including institutional, economic, environmental, climatic, and demographic issues. The future of Japanese agriculture, as well as more general concerns about food security, rural resilience, and sustainable land use in aging societies, depends on an understanding of the intricate dynamics of these issues. The standing of Japan's rice cultivation system and its ability to adjust to socio-ecological and climatic disruptions must be critically examined in light of these intertwining constraints. Researchers are interested in this issue, seeking appropriate methods to mitigate this situation and improve rice varieties to enhance food security. This research is pioneering both academically and socially by utilizing remote sensing technology to detect declining paddy fields and identify high-risk hotspot areas for necessary action. In order to maintain this vital industry, this paper examines the various challenges and uncertainty issues that Japanese rice farmers face, looks into the causes of uncertainty, and evaluates community- and place-based solutions.\u003c/p\u003e \u003cp\u003eThe current status of rice farming in Japan is examined in this paper by comparing the changes in rice fields in the years 2000 and 2025, along with the main causes and factors of challenges and uncertainty, and the ability of institutions, communities, and farmers to adjust to the fast-paced socio-ecological change. The main goal of this research is to detect changes in rice fields, identify abandoned farmland, declining paddy fields, cultivation challenges, and uncertainties, thereby providing location-based solutions relevant to food security policies. The specific objectives of this research are: 1) mapping and differentiating the past and present stands, losses, and changes of agricultural land use in the study sites; 2) predicting probable hotspot areas of rice field decline and identifying the responsible factors/drivers of rice field decline in the context of climatic changes; 3) endorsing place-based solutions for sustainable adaptation based on the predictive modeling approach findings.\u003c/p\u003e \u003cp\u003eThis research investigates the meteorological factors to predict challenges facing future rice production in Toyama, Japan. The use of predictive modeling and remote sensing technology is a novel approach in this field. This interdisciplinary research aims to contribute to SDGs 13 and 2 (climate action and ending hunger, achieving food security, and promoting sustainable agriculture). The findings support policymakers, local communities, and other experts, contributing to Japan's revitalization by providing place-based, policy-relevant solutions.\u003c/p\u003e"},{"header":"2. Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThis research is based on an integrated assessment of Himi City in Toyama Prefecture, Japan, and aims to support data-driven decision-making for sustainable and effective crop management. Himi (氷見市, \u003cem\u003eHimi-shi\u003c/em\u003e) is a city in western Toyama Prefecture, Japan, located at 36\u0026deg;51\u0026prime;24\u0026Prime;N and 136\u0026deg;58\u0026prime;23\u0026Prime;E (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This city had an estimated population of 48,275 in 17,632 households and a population density of 210 persons per km\u0026sup2;. Its total area is 230.56 km\u0026sup2; (89.02 sq mi) (Himi City, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Statistics Bureau Japan, 2020). Himi is famous primarily for its commercial fishing industry. The city was established on August 1, 1952.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHimi is located in Toyama Prefecture's extreme northwest and is bounded to the east by Toyama Bay in the Sea of Japan and to the west and north by Ishikawa Prefecture, which includes the Noto Peninsula. A large portion of the territory is made up of scattered settlements, which are common in this part of Japan. Himi has a humid continental climate (\u003cem\u003eK\u0026ouml;ppen Cfa\u003c/em\u003e) characterized by warm summers and cold winters with plenty of snowfall. The average annual temperature in Himi is 13.9\u0026deg;C, and rainfall is 2409 mm, with September as the wettest month. The temperatures are highest on average in August, at roughly 26.4\u0026deg;C, and lowest in January, at around 2.8\u0026deg;C (Climate data, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection, processing, and accuracy assessment\u003c/h2\u003e \u003cp\u003eIn this study, satellite data of Landsat 5 and 9 were collected from the USGS Earth Explorer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a web-based platform for accessing spatial data. Landsat 5 imagery from the year 2000 and Landsat 9 imagery from 2025 were selected for spatial analysis of land use and land cover (LULC). Both images were acquired in April to maintain seasonal consistency. Initially, the TIFF band files were processed through band composition, followed by supervised classification using a Support Vector Machine (SVM) algorithm using the Maximum Likelihood Classification (MLC) method. The classified raster data was then converted to vector format (polygon) and dissolved to merge adjacent features with similar attributes. Finally, the data were clipped to the study area boundary, and the area of each land cover class was calculated. To ensure high thematic accuracy, 150 representative training samples were collected for each land cover class. Quantitative analysis was then conducted within the GIS environment to calculate the area of each class in square kilometers. The decline in rice fields from 2000 to 2025 was detected. ArcGIS Pro software (version: ArcGIS Pro 3.4.0) and QGIS (QGIS 3.34.12) software were used to process and analyze the data. Ground truthing was conducted by field survey, and the accuracy of the analysis was assessed using the \u003cem\u003ekappa coefficient\u003c/em\u003e. For the accuracy assessment, a random point sampling technique was applied for both the years 2000 and 2025. A total of 40 validation samples were selected for each LULC class, resulting in 240 reference points. These points were converted into KML format and overlaid on high-resolution imagery in Google Earth Pro for visual interpretation and reference data verification. The geographical coordinates of the abandoned rice fields were obtained through a field survey and a Google Earth survey. The abandoned rice fields were identified by comparing the satellite data of 2000 and 2025.\u003c/p\u003e \u003cp\u003eThe future scenarios, challenges, and uncertainties of rice cultivation were predicted using the Maximum Entropy model (Phillips et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The seasonal climate variables (38 variables) were derived from ClimateAP v3.10 across the new emissions scenario from CMIP6, called \u0026ldquo;Shared Socioeconomic Pathways\u0026rdquo; (SSPs) of access-esm-SSP5-8.5 (Wang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These data were extracted, downscaled, and gridded (4 ✕ 4 km) as monthly climate data for the reference from PRISM (Daly et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and WorldClim (Hijmans et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The future climate projections were selected from the General Circulation Models (GCMs) of the Coupled Model Intercomparison Project (CMIP6) included in the IPCC Sixth Assessment Report (AR6) for 2011\u0026ndash;2100.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 These Seasonal variables\u003c/h2\u003e \u003cp\u003eAmong 38 seasonal climate variables, i.e., mean temperature (\u0026deg;C), mean maximum temperature (\u0026deg;C), mean minimum temperature (\u0026deg;C), and precipitation (mm) in winter (December (from the previous year for an individual year), January and February), spring (March, April and May), summer (June, July and August), and autumn (September, October and November) was used as directly calculated variables from the scenario of access-esm-SSP5-8.5. The other derived seasonal climate variables are degree-days below 0\u0026deg;C, degree-days above 5\u0026deg;C, and degree-days below 18\u0026deg;C, for winter (December (from the previous year for an individual year), January and February), spring (March, April and May), summer (June, July and August), and autumn (September, October and November) including the number of frost-free days (NFFD) and precipitation as snow (PAS) (mm) between August in the previous year and July in current year (Wang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStrong correlations among environmental variables may cause multicollinearity concerns, resulting in greater variation in parameter estimations and probable overfitting of the MaxEnt model findings (Xinyi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Halvorsen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, we calculated the correlations of all environmental variables using Pairwise Pearson correlation (Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) to identify and remove highly collinear variables (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.8, 0.85, or 0.9). First, all environmental variables were included in the MaxEnt model to determine variable importance using the jackknife test. If the correlation coefficient between two variables is ≧\u0026thinsp;0.8 (Mason and Perreault, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), the variable with the highest contribution rate was kept for further model analyses. Finally, after examining the above steps, eight environmental variables were converted to the ASCII raster grid format using ArcGIS Pro software. All this eight variables were used for MaxEnt modeling to model the distribution of probable rice field decline areas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 MaxEnt modeling algorithm\u003c/h2\u003e \u003cp\u003eIn this study, MaxEnt (version 3.3.3) modeling was conducted to show the probable areas of rice field abandonment and to investigate the relationship between spatial distribution and the climatic variables. Twenty-five percent of the collected data was randomly selected as testing data, and the remaining seventy-five percent was for training data (Guerra-Coss et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shabani et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The model was performed with 5 replicates to ensure its stability (Wang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe MaxEnt software was used to calculate the testing set's area under the curve (AUC), which ranged from 0 to 1. An AUC value of 0.5 or less denoted poor performance, but an AUC value around 1 suggested flawless prediction (Wang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). The model's performance was assessed using the receiver operating characteristic (ROC) (Phillips et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Manzoor et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The percentage contribution of the variables was assessed in order to investigate the significance of the variables in locating the probable declined rice field distribution locations. Additionally, the relationship between the variables and probability distribution was demonstrated using response curves.\u003c/p\u003e \u003cp\u003eThe software's built-in jackknife test was used to calculate each variable's contribution to the potential distribution area (Liu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hill et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). How much advantage is obtained from each variable alone or from all the factors together is reflected in the built-in jackknife test findings. A higher gain value for a single variable means that the variable contains more information or contributes to the waste distribution area (Wang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe contribution of climatic factors of declination was identified via the Jackknife test of the modeling. The high-risk \"hot spots\" area of rice cultivation was shown in the maps of results section, which will support the policymakers, professionals, or locals to concentrate on further revival of the assessed study sites. The findings of this research will recommend possible suitable scenarios and responsible climatic parameters for rice cultivation in the future.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Land use land cover (LULC) change detection in Himi city\u003c/h2\u003e \u003cp\u003eLULC classification for Himi, Toyama, was accomplished to evaluate environmental changes between 2000 and 2025. The analysis utilized multi-temporal satellite data, specifically Landsat 5 TM for the year 2000 and Landsat 9 OLI-2 for the 2025 assessment. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the LULC maps of Himi city for 2000 and 2025. This map shows the comparison of the land types in the study site. The red colored area represents the built-up area, while the yellow-colored area represents the rice field area in Himi city. The green area stands for vegetation cover, and the blue area is for water bodies. The overall area of Himi city was 230.56 km\u0026sup2;, from where the built-up area increased to 0.47 km\u0026sup2;, and vegetation cover increased by 20.06 km\u0026sup2; from 2000 to 2025 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Some agricultural land or rice fields were converted into plantations and settlements. Also, agricultural land was abandoned in the area, which was converted into an orchard or natural vegetation cover. On the other hand, water bodies have decreased by 0.15 km\u0026sup2;, and rice fields have decreased by 20.01 km\u0026sup2; because of the abandonment of agricultural fields and conversion to orchards.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLULC classification table and area statistics of Himi city, Toyama, Japan\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLULC Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Year 2000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Year 2025)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGain/Loss\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eArea in Sq.km.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt-up area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater bodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation Cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e163.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;20.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRice Cultivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-20.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe following 15 local key areas were selected to predict the future trends of rice fields based on the substantial rice filed abandonment, i.e., 新保 (Shinbo), 論田 (Ron-den), 小窪 (Okubo), 田江 (Tae), 早借 (Hayakashi), 小久米 (Ogume), 日詰 (Hizume), 日名田 (Hinata), 三尾 (Mio), 床鍋 (Tokonabe), 葛葉 (Kuzuba), 久目 (Kume), 触坂 (Furezaka), 岩瀬下岩瀬 (Iwase Shimoiwase ), 見内 (Miuchi). The LULC classification analysis was conducted before conducting MaxEnt modeling in these 15 local areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The comparative results reveal significant shifts in the landscape. Most notably, rice field/agriculture areas showed a substantial decline, dropping from 5.42 sq. km to 3.11 sq. km from 2000 to 2025 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Conversely, bare land experienced a sharp increase, growing from 0.08 sq. km to 0.61 sq. km, suggesting potential land abandonment or preparation for development activities. While evergreen forests saw a minor reduction, deciduous forest areas expanded significantly from 11.05 sq. km to 13.05 sq. km, indicating a notable trend of reforestation or natural succession in the region over the 25 years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLULC classification table and area statistics of 15 local areas in Himi city, Toyama, Japan\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea in 2000 (Sq.km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea in 2025 (Sq.km)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaterbodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBare land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeciduous forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomestead vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRice field/agriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvergreen forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNote\u003c/span\u003e: 新保 (Shinbo), 論田 (Ron-den), 小窪 (Okubo), 田江 (Tae), 早借 (Hayakashi), 小久米 (Ogume), 日詰 (Hizume), 日名田 (Hinata), 三尾 (Mio), 床鍋 (Tokonabe), 葛葉 (Kuzuba), 久目 (Kume), 触坂 (Furezaka), 岩瀬下岩瀬 (Iwase Shimoiwase ), 見内 (Miuchi) areas\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the resulting confusion matrix of the \u003cem\u003eKappa coefficient\u003c/em\u003e, the classification achieved an overall accuracy of 82.92% in 2000, with a \u003cem\u003eк coefficient\u003c/em\u003e of 0.80, indicating strong agreement between the classified map and reference data. In 2025, the accuracy assessment results showed an overall accuracy of 86.67% and a \u003cem\u003eк coefficient\u003c/em\u003e of 0.84, reflecting an improved classification accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Prediction of probable rice field scenario using Maxent modeling\u003c/h2\u003e \u003cp\u003eThe Maxent model output shows an enlarged area of rice field decline or abandonment than the existing area. The output range of the modeling result varied from 0 to 1. The value near zero means lower susceptibility, and the value near one means higher susceptibility. The warmer color shows the area of higher vulnerability to abandonment of the rice field. The average AUC is 0.893, which represents the higher accuracy of the model.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the prediction scenario of rice field abandonment in Himi city using Maxent modeling. The predicted area was classified into four classes. The lower susceptible area ranged up to 0.19, the medium susceptible area ranged from 0.193 to 0.38, the medium-high area ranged from 0.39 to 0.57, and the highly susceptible area ranged from 0.58 to 0.88. The high vulnerability areas are red in color, and the low vulnerability areas are blue in color. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showed that most of the north-western parts of the area are at risk of abandonment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe highly vulnerable areas that fall under the great risk of probable abandonment are Ron-den, Mio, Tokonabe, and Hinata, which comprise an area of 7.40 km\u0026sup2;. The medium and medium-high areas of probable threat to decline include Ogume, Kume, and most of Miuchi area, comprising 10.7 km\u0026sup2;. On the other hand, the less vulnerable area consists of Shinbo, Tae, Hayakashi, part of Iwase Shimoiwase, and the Furezaka area. The total vulnerability area to decline comprised 18.1 km\u0026sup2;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Contribution of climatic variables and their response curves to the model\u003c/h2\u003e \u003cp\u003eThe following table (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) gives estimates of the relative contributions of the environmental variables to the Maxent model. To calculate the first estimate, each iteration of the training algorithm adds the increase in regularized gain to the contribution of the relevant variable. For the second estimate, the values of each environmental variable on training and background data are randomly permuted. The model is reevaluated using the permuted data, and the resulting decline in training AUC is displayed in the table, normalized to percentages. Values shown are averages over replicate runs.\u003c/p\u003e \u003cp\u003eThe highest cumulative contributing variables were precipitation in autumn and summer, combinedly contributing\u0026thinsp;\u0026gt;\u0026thinsp;50%. The third contributing variable is the mean minimum temperature in winter is \u0026gt;\u0026thinsp;12%, and the fourth one is precipitation as snow\u0026thinsp;\u0026gt;\u0026thinsp;12%. The number of frost-free days in spring (nffd) has the highest permutation importance of 25.2. The second and third permutation importance was for the variables of autumn and summer precipitation of 23.4 and 18.9, respectively. The fluctuation or changes of these variables may cause changes or decline in rice fields in the future years.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelative contributions of the environmental variables to the Maxent model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent contribution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePermutation importance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation for autumn (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation for summer (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean minimum temperature (\u0026deg;C) for winter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation as snow between August in the previous year and July in the current year (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of frost-free days for spring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean maximum temperature (\u0026deg;C) for summer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation for spring (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree-days below 0\u0026deg;C for spring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe response curves in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show how each environmental variable influences the Maxent prediction. The curves depict how the anticipated probability of presence changes as each environmental variable is modified, while all other environmental variables remain at their average sample value. The curves depict the mean response of the five replicate Maxent runs (red) and the mean +/- one standard deviation (blue, with two shades for categorical variables). These plots also show the expected suitability's dependence on the selected variable, as well as the dependencies caused by correlations between the selected variable and others.\u003c/p\u003e \u003cp\u003ePrecipitation of autumn has the highest influences on the model when the value is 635 to 639 mm. Again, precipitation of summer has the highest influences on the model when the value is 584 to 596 mm with some fluctuations. The mean minimum temperature in winter impacts the model output when the range is 1.0 to 1.8\u0026deg;C, with fluctuations inside the graph. When the number of frost-free days for spring is lower, i.e., 81, the impact is higher, and it reduces with the increase in the number of days. Degree-days below 0\u0026deg;C for spring, precipitation as snow, and Mean maximum temperature (\u0026deg;C) for summer has similar concave trend of effects on the model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe findings of the jackknife test of variable relevance are displayed in the following Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The mean minimum temperature for winter appears to provide the most useful data when utilized alone, as it is the environmental variable with the biggest gain. Precipitation during autumn appears to have the greatest information that isn't present in the other variables because it is the environmental variable that reduces the gain the most when it is removed. The values displayed are averages across multiple runs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Decline of rice fields and the responsible climatic factors\u003c/h2\u003e \u003cp\u003eSignificant changes in the landscape have occurred between 2000 and 2025, according to comparative land-use and land-cover studies. Most significantly, rice fields and agricultural areas decreased abruptly from 64 km\u0026sup2; to 26 km\u0026sup2;, indicating widespread agricultural abandonment linked to depopulation, aging farming populations, and diminished economic viability of small-scale rice cultivation in Japan (MAFF, 2021; Kobayashi et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conversely, the area of vegetation cover expanded from 143 km\u0026sup2; to 163 km\u0026sup2;, signifying transitory land conditions sometimes associated with land abandonment, postponed redevelopment, or colonization of vegetation species (Seto et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Forest cover dynamics further reveal ecological succession processes. Deciduous woods significantly increased while evergreen forest areas slightly decreased. This trend is consistent with secondary forest regeneration following agricultural abandonment, where deciduous species dominate early- to mid-successional stages in rural Japan (Takeuchi, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kobayashi et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmploying the MaxEnt modeling approach, this study identified key climatic variables influencing the spatial distribution and persistence of rice fields. MaxEnt is especially adept for this investigation as it can estimate probabilities utilizing presence-only data and intricate, non-linear correlations between rice occurrence and environmental factors. Model results indicated that variables related to precipitation patterns (e.g., precipitation of autumn and summer) are the most influential, reflecting rice\u0026rsquo;s strong dependence on consistent water availability. Temperature-related factors, notably mean minimum temperature in the winter season, correspondingly showed an adverse effect by fostering evapotranspiration and heat stress, which reduce yields and inhibit continued cultivation.\u003c/p\u003e \u003cp\u003eInadequate or inconsistent summer and autumn precipitation restricts water availability throughout critical growth and harvesting phases, heightening drought stress and diminishing yields, whereas excessive rainfall can harm crops and postpone harvests (IRRI, 2013; Yoshida, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Increasing average minimum winter temperatures, a significant indicator of global warming, diminish snow accumulation in temperate regions such as Japan, hence undermining snowmelt-dependent irrigation systems crucial for spring paddy flooding (Atmowidjojo et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, an augmentation in frost-free days modifies rice phenology, hastens growth cycles, and may elevate pest and disease pressure, thus lowering long-term field viability (Kondo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Collectively, these climatic changes lead to decreasing rice field viability and land abandonment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Implications for place-based solutions for sustainable adaptation\u003c/h2\u003e \u003cp\u003eThe regional variability of environmental factors impacting land-use change is highlighted by the MaxEnt modeling results, underscoring the necessity of place-based adaptation measures. Areas with high adaptability driven by climatic variables such as seasonal precipitation, winter minimum temperature, and frost-free days indicate locations where conventional land uses, including rice agriculture, remain viable under changing climate circumstances (Phillips et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Elith et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In these high-probability zones of decline, targeted assistance for climate-resilient farming methods can increase adaptive capacity and minimize agricultural land abandonment. In general, MaxEnt-informed place-based solutions enhance long-term sustainability by aligning adaptation strategies to local social and environmental conditions.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe high-risk \"hot spots\" areas of rice field abandonment are stated as medium-high and high vulnerability. These findings will support policymakers, professionals, or locals to concentrate and take measures on further revival of the rice-cultivated area in the assessed study sites. The findings of this research recommend that specialists and government bodies focus on the protection of rice fields abandonment based on the predicted scenarios of vulnerability in rice fields. Further research is required to predict the future trend based on the socio-economic conditions. Overall, the MaxEnt-based results showed that sustainable land-use planning requires place-based, climate-informed adaptation techniques. Decision-makers can improve resilience, protect biodiversity, and minimize maladaptive land-use change under persistent climatic pressure by coordinating development constraints, ecological restoration, and agricultural support with locally dominating environmental factors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Public Interest Incorporated Foundation \u0026lsquo;Toyama Daiichi Bank Scholarship Foundation\u0026rsquo;. This foundation had no role in study design; data collection, analysis, and interpretation; the writing of the report; or the decision to submit the article for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from https://climateap.net/, https://web.climateap.net, and https://worldclim.org/\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSharmin Shishir wrote the main manuscript text, conducted the MaxEnt modeling using climate change scenarios, and prepared figures 1, 4, 5, and 6. Naiem Mollah performed the spatial analysis of land use and land cover change and prepared the figures 2 and 3. All authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the Public Interest Incorporated Foundation \u0026lsquo;Toyama Daiichi Bank Scholarship Foundation\u0026rsquo; for funding this research. We thank Mr. Wikas Younis for supporting the administrative data collection. We are also grateful to Haque Md Ariful and Md Masuk Mowla Aunkur for their assistance during the statistical analysis.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAtmowidjojo AC, Huang CK, Arima Y (2024) Japan Initiatives of Climate Change Adaptation for Paddy Rice Commodity. 1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hdl.handle.net/2433/286777\u003c/span\u003e\u003cspan address=\"http://hdl.handle.net/2433/286777\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClimate data (2025) Data and graphs for weather \u0026amp; climate in Himi. Date accessed: October 7th, 2025, 15:08 pm. 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IRRI. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://books.irri.org/9711040522_content.pdf\u003c/span\u003e\u003cspan address=\"http://books.irri.org/9711040522_content.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeigler RS, Barclay A (2008) The Relevance of Rice. Rice 1:3\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12284-008-9001-z\u003c/span\u003e\u003cspan address=\"10.1007/s12284-008-9001-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-systems-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ensr","sideBox":"Learn more about [Environmental Systems Research](http://environmentalsystemsresearch.springeropen.com)","snPcode":"40068","submissionUrl":"https://submission.nature.com/new-submission/40068/3","title":"Environmental Systems Research","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate change, land-use and land-cover, Himi city, MaxEnt model, sustainable agriculture","lastPublishedDoi":"10.21203/rs.3.rs-8736594/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8736594/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change and global warming are altering weather patterns, threatening crop productivity. The resultant impact can be food insecurity. Japan's rice farms are in a precarious situation due to a multifaceted crisis driven by climate change. Utilizing remote sensing and the MaxEnt predictive modeling approach to identify declining rice fields can significantly enhance agricultural potential and provide a pertinent solution for integrated, place-based sustainable management in the regions. This technique suggests the likely abandonment of rice fields owing to climate change by identifying the principal factors contributing. The research aims to explore rice cultivation's scenarios, challenges, and uncertainties to bring policy-relevant place-based solutions for food security in Himi city, Toyama prefecture, Japan. The land-use and land-cover analysis shows that rice fields have decreased by 20.01 km\u0026sup2; from 2000 to 2025 in Himi city. The results indicate that autumn and summer precipitation, along with mean minimum winter temperature, are the primary environmental factors influencing the potential abandonment distribution of rice fields, accounting for a cumulative contribution of \u0026gt;\u0026thinsp;63%. Rice fields are expected to decline northwestward, from 46.178km\u0026sup2; to 26.17km\u0026sup2;. High probability for declined regions of rice field is predicted from \u0026gt;\u0026thinsp;3km\u0026sup2; to \u0026gt;\u0026thinsp;7km\u0026sup2;. This decrease in rice fields and probable future decrease present potential risks for rice cultivation in the Himi city. Future efforts for mitigating the effects of climate change on rice planting adaptation should include sowing period changes, cultivar innovation, and rationalized application of fertilizer. These adaptable solutions are critical for advancing rural revitalization initiatives and promoting the integrated advancement of sustainable socio-environmental and agricultural development. This multi-disciplinary research seeks to contribute to Target 13 (climate action) of the Sustainable Development Goals (SDGs) and SDG 2, which aims to eradicate hunger, achieve food security, improve nutrition, and promote sustainable agriculture.\u003c/p\u003e","manuscriptTitle":"Assessing and predicting the challenges and uncertainties of rice cultivation due to climate change effects in Toyama Prefecture, Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 15:08:14","doi":"10.21203/rs.3.rs-8736594/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-23T18:50:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T08:45:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-07T13:53:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293667963365739330721600684164976989191","date":"2026-02-07T13:04:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301440177029016555553282301843593586964","date":"2026-02-06T01:29:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176223760379755916280062429900343004751","date":"2026-02-05T02:55:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-05T01:18:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-05T01:14:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-04T14:50:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Systems Research","date":"2026-01-30T03:22:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-systems-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ensr","sideBox":"Learn more about [Environmental Systems Research](http://environmentalsystemsresearch.springeropen.com)","snPcode":"40068","submissionUrl":"https://submission.nature.com/new-submission/40068/3","title":"Environmental Systems Research","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"08bb301f-fc7c-484c-adc1-4ff7d71956f6","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:00:37+00:00","versionOfRecord":{"articleIdentity":"rs-8736594","link":"https://doi.org/10.1186/s40068-026-00474-2","journal":{"identity":"environmental-systems-research","isVorOnly":false,"title":"Environmental Systems Research"},"publishedOn":"2026-04-05 15:57:44","publishedOnDateReadable":"April 5th, 2026"},"versionCreatedAt":"2026-02-09 15:08:14","video":"","vorDoi":"10.1186/s40068-026-00474-2","vorDoiUrl":"https://doi.org/10.1186/s40068-026-00474-2","workflowStages":[]},"version":"v1","identity":"rs-8736594","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8736594","identity":"rs-8736594","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}