From scenario to roadmap: Design and evaluation of a web-based participatory watershed planning system for optimizing multistage implementation plans of management practices under stepwise investment.

Planning multistage implementation plans, or roadmaps, based on the spatial distribution of a best management practice (BMP) scenario is essential for achieving watershed management goals under realistic conditions, such as stepwise investment plans that involve multiple stakeholders, including investors, economic and environmental beneficiaries. The state-of-the-art BMP roadmap optimization method can address this need for optimization but is over-specialized and complex to non-expert stakeholders. This study designed a user-friendly web-based participatory watershed planning system to assist a diverse group of stakeholders in reaching a consensus on optimal roadmaps. The participatory process of stakeholders includes iteratively proposing stepwise investment constraints, submitting roadmap optimization tasks, analyzing spatiotemporal results from multiple perspectives, and selecting preferred roadmaps. The proposed system design separates the participatory process of non-expert stakeholders from the specialized modeling process of constructing simulation-optimization tools for BMP roadmaps, which is done in advance by professional modelers and encapsulated as webservices on the server side. The webservices expose a small set of essential parameters to lower barriers to use. The interactive participatory process is presented to stakeholders through web browsers with an easy-to-use interface. The system design was evaluated by implementing an agricultural watershed planning system for soil erosion reduction and conducting a role-playing experiment involving three groups of stakeholders with different standpoints in proposing investment constraints. The experimental results show that the optimal roadmap sets exhibit progressive improvements across three-round optimizations started by different stakeholders, effectively capturing the varying perspectives of stakeholders and facilitating consensus-building among them. The idea of system design and example implementation can serve as a valuable reference for developing related user-friendly environmental decision support systems.

Transformer-Based Semantic Segmentation for Extraction of Building Footprints from Very-High-Resolution Images.

Semantic segmentation with deep learning networks has become an important approach to the extraction of objects from very high-resolution remote sensing images. Vision Transformer networks have shown significant improvements in performance compared to traditional convolutional neural networks (CNNs) in semantic segmentation. Vision Transformer networks have different architectures to CNNs. Image patches, linear embedding, and multi-head self-attention (MHSA) are several of the main hyperparameters. How we should configure them for the extraction of objects in VHR images and how they affect the accuracy of networks are topics that have not been sufficiently investigated. This article explores the role of vision Transformer networks in the extraction of building footprints from very-high-resolution (VHR) images. Transformer-based models with different hyperparameter values were designed and compared, and their impact on accuracy was analyzed. The results show that smaller image patches and higher-dimension embeddings result in better accuracy. In addition, the Transformer-based network is shown to be scalable and can be trained with general-scale graphics processing units (GPUs) with comparable model sizes and training times to convolutional neural networks while achieving higher accuracy. The study provides valuable insights into the potential of vision Transformer networks in object extraction using VHR images.

Mapping high resolution National Soil Information Grids of China.

Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a high-performance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties (pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately 5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate (Model Efficiency Coefficients from 0.71 to 0.36) at 0-5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.

Behind the Silence of the Professional Classroom in Universities: Formation of Cognition-Practice Separation among University Students-A Grounded Theory Study in China.

Classroom silence is a negative form of classroom performance that is particularly prominent in the Chinese learner population. Existing research has mainly explored the silence phenomenon among Chinese university students in two types of learning contexts: overseas university classrooms and foreign language classrooms at local universities, without focusing on the Chinese undergraduates' reticence in courses mediated by native language at domestic universities. However, the last type is the most common habitat for Chinese university students' learning in higher education. Therefore, a sample of Chinese undergraduates majoring in education (n = 394) was recruited to determine the mechanisms of silence formation in professional classrooms. This study was based on grounded theory and in-depth interviews, and the recorded material was processed using NVivo 12. After a series of steps including open coding, axial coding, selective coding, and theoretical saturation testing, the core feature of the phenomenon of silence in professional classrooms of Chinese university students majoring in education was found to be the separation of students' cognition and speaking practice. Then, a theoretical model of the formation and development of the phenomenon of classroom silence in professional classrooms of these undergraduates was constructed. The study showed that these university students had professional perceptions of classroom silence and displayed strong opposition to it, but they continued to maintain silent classroom behavior under the combined influence of individual characteristics, classroom experience, and learning adjustment. Following this, implications for existing research and suggestions for future practice are discussed.

A local model based on environmental variables clustering for estimating foliar phosphorus of rubber trees with vis-NIR spectroscopic data.

Existing local models based on multiple environmental variables clustering (LM-MEVC) treat the influences of environmental factors on leaf phosphorus concentration (LPC) of rubber trees (Hevea brasiliensis) equally when grouping samples. In fact, the effects that environmental factors assert on LPC are different. So, environmental factors need to be treated differently so that the different effects can be taken into consideration when dividing samples into clusters or groups. According to this basic idea, a local model based on weighted environmental variables clustering (LM-WEVC) was developed. This approach consists of four steps. Firstly, the most important environmental variables that influence LPC were selected. Then, the weights of the selected environmental variables were determined. In the following, the selected environmental variables were weighted and used as clustering variables to group samples. Finally, within each cluster or group of samples, an estimation model was established. In order to verify its effectiveness in predicting LPC of rubber trees, the proposed method was applied to a case study in Hainan Island, China. Rubber tree (cultivar CATAS-7-33-97) leaf samples were collected from three different sampling periods. Spectral reflectance of the collected leaf samples was measured using an ASD spectroradiometer, FieldSpec 3. Leaf samples collected from the three different sampling periods were used separately to test LM-WEVC. Coefficient of determination (R2), root mean squared error (RMSE), and ratio of prediction deviation (RPD) were employed as evaluation criterion. Performance of LM-WEVC was compared with that of the existing LM-MEVC. Results indicated that for the three sampling periods, the prediction accuracies of LM-WEVC were always higher than those of LM-MEVC. The values of R2 and RPD for LM-WEVC were increased by 8.15%-36.68%, and by 11.33%-59.40% respectively, while values of RMSE were reduced by 9.09%-37.5%, compared with those for LM-MEVC. These results demonstrate that LM-WEVC was effective in estimating LPC of rubber trees, and also confirmed our hypothesis that environmental factors unequally influenced LPC of rubber trees.

Predicting isoscapes based on an environmental similarity model for the geographical origin of Chinese rice.

The authentication of geographical origin of food is important using stable isotope analysis. However, the isotopic databank is still short of comprehensive. The isoscapes model based on environmental similarity is used for the first time to predict the geospatial distribution of δ13C, δ2H and δ18O in Chinese rice in 2017 and 2018. 794 rice samples in 2017 were used to build isoscapes model. Independent verification shows that the predicted isotope distribution from this new approach is of high accuracy, with a root mean square error (RMSE) of 0.51 ‰, 7.09 ‰ and 2.06 ‰ for δ13C, δ2H and δ18O values for 2017, respectively. Our results indicate that it is possible to predict the spatial distribution of stable isotopes in rice using an isoscapes model based on environmental similarity. This novel strategy can enrich and complement a stable isotope reference database for rice origin identification at regional scale.

Impacts of underwater topography on the distribution of microplastics in lakes: A case from Dianchi Lake, China.

The spatial distribution of microplastics and the factors influencing their distribution in lakes are important aspects of plastics pollution studies. This study investigated the impacts of lake underwater topography on the spatial distribution of microplastics in Dianchi Lake in China. Data on spatial distribution of microplastics were obtained by pump sampling, microscopic examination, and polymer identification. Parameters of underwater topography were extracted from an isobaths map of Dianchi Lake. The relationships between underwater topography and the abundance of microplastics were analyzed. The results showed that for the northern part of the lake, water depth, slope gradient, relief, roughness and surface curvature have significant relationships with the spatial distribution of microplastics. In the southern part, only roughness showed a significant relationship. The roughness is the only important factor which impacts the microplastics distribution in both parts of the lake and the whole lake. These differences between the northern part and the southern part of the lake are related to the stronger circular currents in the southern part of the lake. These results showed that the impacts of underwater topography manifest themselves well when lake currents are weak, and these impacts are reduced or muted when lake currents are strong. Our research results provide a good reference for understanding distribution and migration principle of microplastics in lakes.

Spatial variation of particulate black carbon, and its sources in a large eutrophic urban lake in China.

Black carbon (BC), characterized by high aromaticity and stability, has been recognized as a substantial fraction of the carbon pool in soil and sediment. The effect of BC on the particulate organic carbon (POC) pool in lake water, which is an important medium of carbon transmission and transformation, has not been thoroughly studied. The investigations of BC composition and distribution, POC, polycyclic aromatic hydrocarbons (PAHs), and stable carbon and nitrogen isotopes were conducted in a eutrophic urban lake, Taihu Lake, which is the third largest freshwater lake in China. The results indicate that the BC is composed of 55 ± 12% char and 45 ± 12% soot and accounted for 12 ± 6% of POC (the maximum value is 31%). The comparatively high levels of BC and char are distributed in the northern Taihu Lake, especially in Meiliang Bay (0.72 ± 0.38 mg L-1 and 0.45 ± 0.24 mg L-1). The distribution of soot presents a declining trend from the lakeshore to the central lake, particularly in the northern, western, and southern lakes. Source apportionment results from positive matrix factorization of PAHs suggest that consumption of fossil fuel (79 ± 20%) is the dominant source of BC, which agrees with the low ratio of char/soot (1.41 ± 0.71) and relatively depleted δ13C. The covariation of BC and PAHs and terrestrial dissolved organic carbon indicate that the effect of terrestrial input significantly regulates the distribution of BC in Taihu Lake, which is reflected in the high BC value along the lakeshore.

Anthropogenic-Driven Alterations in Black Carbon Sequestration and the Structure in a Deep Plateau Lake.

The continuous flux of organic carbon (OC) from terrestrial ecosystems into inland water is an important component of the global carbon cycle. The buried OC pool in inland water sediments is considerable, and black carbon (BC) is a significant contributor to this OC pool because of the continuous growth in BC emissions. Therefore, determining the effect of BC on total OC burial and variations in the structure of BC during the burial process will contribute significantly to our understanding of lacustrine carbon cycling. This study investigated BC burial and its structural variations in response to anthropogenic drivers using four dated sedimentary cores from a deep plateau lake in China. The BC burial rate rose from 0.96 ± 0.64 g·m-2·y-1 (mean of sedimentary cores pre-1960s) to 4.83 ± 1.25 g·m-2·y-1 (after 2000), which is a 5.48 ± 2.12-fold rise. The increase of char was similar to those of BC. The growth rate of soot was 7.20 ± 4.30 times, which is higher than that of BC and char, increasing from 0.12 ± 0.08 to 0.64 ± 0.23 g·m-2·y-1. There was a decreasing trend in the ratio of char and soot at a mean rate of 62.8 ± 6.46% (excluding core 3) in relation to increased fossil fuel consumption. The contribution of BC to OC burial showed a significant increasing trend from the past to the present, particularly in cores 3 and 4, and the mean contribution of the four cores was 11.78 ± 2.84%. Source tracer results from positive matrix factorization confirmed that the substantial use of fossil fuels has promoted BC burial and altered the BC structure. This has resulted in BC with a higher aromatic content in the lake sediment, which exhibits reduced reactivity and increased stability. The strong correlation between BC and allochthonous total OC indicates that the input pathways of the buried BC in this plateau lake sediment were terrestrial surface processes and not atmospheric deposition.

Cross-Year Reuse of Historical Samples for Crop Mapping Based on Environmental Similarity.

Crop classification maps are fundamental data for global change research, regional agricultural regulation, fine production, and insurance services. The key to crop classification is samples, but it is very time-consuming in annual field sampling. Therefore, how to use historical samples in crop classification for future years at a lower cost is a research hotspot. By constructing the spectral feature vector of each historical sample in the historical year and its neighboring pixels in the target year, we produced new samples and classified them in the target year. Specifically, based on environmental similarity, we first calculated the similarities of every two pixels between each historical year and target year and took neighboring pixels with the highest local similarity as potential samples. Then, cluster analysis was performed on those potential samples of the same crop, and the class with more pixels is selected as newly generated samples for classification of the target year. The experiment in Heilongjiang province, China showed that this method can generate new samples with the uniform spatial distribution and that the proportion of various crops is consistent with field data in historical years. The overall accuracy of the target year by the newly generated sample and the real sample is 61.57 and 80.58%, respectively. The spatial pattern of maps obtained by two models is basically the same, and the classification based on the newly generated samples identified rice better. For areas with majority fields having no rotation, this method overcomes the problem of insufficient samples caused by difficulties in visual interpretation and high cost on field sampling, effectively improves the utilization rate of historical samples, and provides a new idea for crop mapping in areas lacking field samples of the target year.

CN-China: Revised runoff curve number by using rainfall-runoff events data in China.

The curve number (CN) method developed by the United States Department of Agriculture (USDA) in 1954 is the most common adopted method to estimate surface runoff. For years, applicability of the CN method is a conundrum when implementing to other countries. Specifically, countries with more complex natural environment may require more dedicated adjustments. Therefore, the current CN look-up table provided by USDA might not be appropriate and could be questionable to be applied directly to regions elsewhere. Some studies have been conducted to modify CN values according to specified natural characteristics in scattered regions of mainland China. However, an integral and representative work is still not available to address potential concerns in general matters. In this study, a large set of rainfall-runoff monitoring data were collected to adjust CN values in 55 study sites across China. The results showed that the revised CN values are largely different from CN look-up table provided by USDA, which would lead to huge errors in runoff estimation. In this study, the revised CN (dubbed CN-China) provides better reference guidelines that are suitable for most natural conditions in China. In addition, scientists and engineers from other parts of the world can take advantage of the proposed work to enhance the quality of future programs related to surface runoff estimation.

Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble.

The major target of this study is to design two novel hybrid integration artificial intelligent models, which are denoted as LADT-Bagging and FPA-Bagging, for modeling landslide susceptibility in the Youfanggou district (China). First of all, we prepared a geospatial database in the study area, including 79 landslide points that were divided into a training and validating dataset and 14 landslide conditioning factors. Second, the Support Vector Machines classifier (SVMC) approach was adapted to analyze the predictive capability of the landslide predisposing factors in each method. Then, a multicollinearity analysis using TOL and VIF parameters and Pearson's correlation coefficient methods were applied to verify the multicollinearity and correlation between these factors. Third, the LADT-Bagging and FPA-Bagging models were built by the integration of the LogitBoost alternating decision trees (LADT) with the Bagging ensemble and Forest by Penalizing Attributes (FPA) with the Bagging ensemble, respectively. Besides, heuristic tests were also applied to identify the appropriate values of each model's parameters in order to obtain the best programmer. Finally, for the training dataset, the results reveal that the LADT-Bagging model acquire the largest AUC value (0.980), smallest standard error (SE) (0.0134), narrowest 95% confidence interval (CI) (0.920-0.999), highest accuracy value (AV) (91.03%), highest specificity (94.44%), highest sensitivity (88.10%), highest F-measure (0.9115), lowest MAE (0.2016), lowest RMSE (0.2653), and highest Kappa (0.8205). About the result of validating dataset, it reveal that the LADT-Bagging model acquire the largest AUC value (0.781), the smallest SE (0.0539), the narrowest 95% CI (0.673-0.867), highest AV (71.19%), highest specificity (74.29%), highest sensitivity (69.77%), highest F-measure (0.7195), lowest MAE (0.3509), lowest RMSE (0.4335), and highest Kappa (0.4359). The results indicate that the LADT-Bagging model outperforms the FPA-Bagging, LADT and FPA models. Furthermore, the results of a Wilcoxon signed-rank test demonstrate that LADT-Bagging is significantly statistically different from other models. Therefore, in this study, the proposed new models are useful tools for land use planners or governments in high landslide risk areas.

Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality.

Quality monitoring is important for farmland protection. Here, high-resolution remote sensing data obtained by unmanned aerial vehicles (UAVs) and long-term ground sensing data, obtained by wireless sensor networks (WSNs), are uniquely suited for assessing spatial and temporal changes in farmland quality. However, existing UAV-WSN systems are unable to fully integrate the data obtained from these two monitoring systems. This work addresses this problem by designing an improved UAV-WSN monitoring system that can collect both high-resolution UAV images and long-term WSN data during a single-flight mission. This is facilitated by a newly proposed data transmission optimization routing protocol (DTORP) that selects the communication node within a cluster of the WSN to maximize the quantity of data that can be efficiently transmitted, additionally combining individual scheduling algorithms and routing algorithms appropriate for three different distance scales to reduce the energy consumption incurred during data transmission between the nodes in a cluster. The performance of the proposed system is evaluated based on Monte Carlo simulations by comparisons with that obtained by a conventional system using the low-energy adaptive clustering hierarchy (LEACH) protocol. The results demonstrate that the proposed system provides a greater total volume of transmitted data, greater energy utilization efficiency, and a larger maximum revisit period than the conventional system. This implies that the proposed UAV-WSN monitoring system offers better overall performance and enhanced potential for conducting long-term farmland quality data collection over large areas in comparison to existing systems.

The Optimal Image Date Selection for Evaluating Cultivated Land Quality Based on Gaofen-1 Images.

This study proposes a method for determining the optimal image date to improve the evaluation of cultivated land quality (CLQ). Five vegetation indices: leaf area index (LAI), difference vegetation index (DVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI) are first retrieved using the PROSAIL model and Gaofen-1 (GF-1) images. The indices are then introduced into four regression models at different growth stages for assessing CLQ. The optimal image date of CLQ evaluation is finally determined according to the root mean square error (RMSE). This method is tested and validated in a rice growth area of Southern China based on 115 sample plots and five GF-1 images acquired at the tillering, jointing, booting, heading to flowering, and milk ripe and maturity stage of rice in 2015, respectively. The results show that the RMSEs between the measured and estimated CLQ from four vegetation index-based regression models at the heading to flowering stage are smaller than those at the other growth stages, indicating that the image date corresponding with the heading to flowering stage is optimal for CLQ evaluation. Compared with other vegetation index-based models, the LAI-based logarithm model provides the most accurate estimates of CLQ. The optimal model is also driven using the GF-1 image at the heading to flowering stage to map CLQ of the study area, leading to a relative RMSE of 14.09% at the regional scale. This further implies that the heading to flowering stage is the optimal image time for evaluating CLQ. This study is the first effort to provide an applicable method of selecting the optimal image date to improve the estimation of CLQ and thus advanced the literature in this field.

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data.

Rapid and efficient assessment of cultivated land quality (CLQ) using remote sensing technology is of great significance for protecting cultivated land. However, it is difficult to obtain accurate CLQ estimates using the current satellite-driven approaches in the pressure-state-response (PSR) framework, owing to the limitations of linear models and CLQ spectral indices. In order to improve the estimation accuracy of CLQ, this study used four evaluation models (the traditional linear model; partial least squares regression, PLSR; back propagation neural network, BPNN; and BPNN with genetic algorithm optimization, GA-BPNN) to evaluate CLQ for determining the accurate evaluation model. In addition, the optimal satellite-derived indicator in the land state index was selected among five vegetation indices (the normalized vegetation index, NDVI; enhanced vegetation index, EVI; modified soil-adjusted vegetation index, MSAVI; perpendicular vegetation index, PVI; and soil-adjusted vegetation index, SAVI) to improve the prediction accuracy of CLQ. This study was conducted in Conghua District of Guangzhou, Guangdong Province, China, based on Gaofen-1 (GF-1) data. The prediction accuracies from the traditional linear model, PLSR, BPNN, and GA-BPNN were compared using observations. The results demonstrated that (1) compared with other models (the traditional linear model: R2 = 0.14 and RMSE = 91.53; PLSR: R2 = 0.33 and RMSE = 74.58; BPNN: R2 = 0.50 and RMSE = 61.75), the GA-BPNN model based on EVI in the land state index provided the most accurate estimates of CLQ, with the R2 of 0.59 and root mean square error (RMSE) of 56.87, indicating a nonlinear relationship between CLQ and the prediction indicator; and (2) the GA-BPNN-based evaluation approach of CLQ in the PSR framework was driven to map CLQ of the study area using the GF-1 data, leading to an RMSE of 61.44 at the regional scale, implying that the GA-BPNN-based evaluation approach has the potential to map CLQ over large areas. This study provides an important reference for the high-accuracy prediction of CLQ based on remote sensing technology.

The relevant range of scales for multi-scale contextual spatial modelling.

Spatial autocorrelation in the residuals of spatial environmental models can be due to missing covariate information. In many cases, this spatial autocorrelation can be accounted for by using covariates from multiple scales. Here, we propose a data-driven, objective and systematic method for deriving the relevant range of scales, with distinct upper and lower scale limits, for spatial modelling with machine learning and evaluated its effect on modelling accuracy. We also tested an approach that uses the variogram to see whether such an effective scale space can be approximated a priori and at smaller computational cost. Results showed that modelling with an effective scale space can improve spatial modelling with machine learning and that there is a strong correlation between properties of the variogram and the relevant range of scales. Hence, the variogram of a soil property can be used for a priori approximations of the effective scale space for contextual spatial modelling and is therefore an important analytical tool not only in geostatistics, but also for analyzing structural dependencies in contextual spatial modelling.

Long-term variation of phytoplankton biomass and physiology in Taihu lake as observed via MODIS satellite.

Estimation of phytoplankton biomass (noted as phytoplankton carbon, Cphyto) and evaluation of phytoplankton physiology is central to the estimation of primary productivity and the carbon cycle. This issue has been widely considered in oceans but not in inland water. Here, we develop experiential and semi-analytical models, which validated by independent in situ measurement data, respectively, to derive Cphyto and phytoplankton absorption coefficient at 675 nm (aph(675)) from MODIS. The effects of nutrients and temperature on the seasonal variation of phytoplankton physiology were assessed through a novel proxy of Cphyto to aph(675) ratio (Cphyto/aph(675)) over the Lake Taihu, the third largest lake in China. Significant seasonal climatological cycles of Cphyto, aph(675) and Cphyto/aph(675) were observed in Lake Taihu, especially in Meiliang Bay and Zhushan Bay, where algal blooms occur frequently. The highest Cphyto and aph(675) values were observed in summer due to the growth of phytoplankton biomass and chlorophyll-a concentration. Lower values were observed in winter and spring, which are characterized by relatively high total nitrogen levels and low irradiance, owing to the low temperature astricts the algae growth. However, the Cphyto/aph(675) shows an opposite trend compared to Cphyto and aph(675), which have high values in winter and low values in summer. The analysis of Cphyto, aph(675) and Cphyto/aph(675) with total phosphorus (TP) levels and temperature indicates that TP are the main positive driver of the increase in Cphyto and aph(675) and negatively regulate Cphyto/aph(675). Warming promotes an increase in Cphyto and aph(675) and restricts Cphyto/aph(675) in summer. Biomass and nutrient levels are the primary drivers of the decrease of Cphyto/aph(675) in such a typical eutrophic lake. The results present some new findings compared to previous oceanic studies and expand our knowledge in the study of phytoplankton biomass and physiology in eutrophic lakes.

Vulnerability of cotton subjected to hail damage.

This paper establishes the quantitative relationships between hail fall parameters and crop damages by examining the impacts of 49 hail hazard scenarios on cotton in the bud stage and boll stage. This study utilizes simulated cotton hail hazard to analyze the following data: hail size, hail fall density, and crop damages (i.e., defoliation rate, branch breaking rate, and the fruit falling rate). The results are as follows: 1) cotton vulnerability increased via an increase in crop damages as the hail hazard magnitude increased; 2) crop damages exhibit significant logistic relationships with hail diameter and hail fall density, and the fit was better at the bud stage than at the boll stage; 3) cotton is more vulnerable to hail hazard at the bud stage than at the boll stage, and the bud stage is a critical period for cotton hail disaster prevention and mitigation; and 4) damages to cotton plant at the bud stage and boll stage were less sensitive to hail size from hail fall density. Thus, we suggest that hail diameter can be used as the priority indicator to predict hail-induced crop damages. These results provide a sound basis for developing a comprehensive evaluation of hail damage in cotton, which is crucial for promoting sustainable cotton production. We recommend that the impacts of hail-induced crop damages on yield and fiber quality need to be addressed further in future studies.

Global sensitivity analysis of the APSIM-Oryza rice growth model under different environmental conditions.

This study conducted the global sensitivity analysis of the APSIM-Oryza rice growth model under eight climate conditions and two CO2 levels using the extended Fourier Amplitude Sensitivity Test method. Two output variables (i.e. total aboveground dry matter WAGT and dry weight of storage organs WSO) and twenty parameters were analyzed. The ±30% and ±50% perturbations of base values were used as the ranges of parameter variation, and local fertilization and irrigation managements were considered. Results showed that the influential parameters were the same under different environmental conditions, but their orders were often different. Climate conditions had obvious influence on the sensitivity index of several parameters (e.g. RGRLMX, WGRMX and SPGF). In particular, the sensitivity index of RGRLMX was larger under cold climate than under warm climate. Differences also exist for parameter sensitivity of early and late rice in the same site. The CO2 concentration did not have much influence on the results of sensitivity analysis. The range of parameter variation affected the stability of sensitivity analysis results, but the main conclusions were consistent between the results obtained from the ±30% perturbation and those obtained the ±50% perturbation in this study. Compared with existing studies, our study performed the sensitivity analysis of APSIM-Oryza under more environmental conditions, thereby providing more comprehensive insights into the model and its parameters.