International audience | CONTEXT: Pollution of water resources by substances emitted by agriculture, such as nitrate, pesticides, pharmaceutical residues, fecal microorganisms and microplastics, remains a crucial issue. To assess the effectiveness of pollution-mitigation projects, water quality is usually monitored at the watershed scale. In parallel, farmers and agricultural advisors use mainly means-based indicators to assess farm sustainability. In Brittany, France, the Terres de Sources project addresses the following issues: (i) individual farmers cannot assess effects of changes in their practices using result-based water-quality indicators at the watershed outlet and (ii) means-based indicators provide little information about local water quality.OBJECTIVE: The aim of this project was to gather together researchers, farmers and advisors to build operational result-based indicators that would allow farmers to estimate on-farm emissions of pollutants to water. This article highlights the implementation and outputs of a collective design process to create such indicators.METHODS: The Knowledge-Concepts-Proposals design method was implemented to explore ideas around the initial concept of “result-based water-quality indicators at the farm scale”. The method’s design process has four steps, from initiation to outputs. Emerging ideas of indicators were classified in four categories and we finally selected scientifically relevant and achievable indicators. The methods for measuring these indicators were worked during the final phase of the design process.RESULTS AND CONCLUSIONS: The main results of the design process were (i) a set of result-based indicators focused on nitrate and pesticides and related to chemical measurements and bioindicators, (ii) the development of phases of “farm characterization” and “on-farm monitoring strategy” to understand water circulation, the relevant “types of water” to sample and suitable on-farm monitoring locations. In addition, breakthrough ideas have emerged but not exploited in this project; they were related to indicators based on senses and on exposure of livestock to pollutants. Despite fixation effects, the group was actively involved in the design process and in the proposal of subsequent prototype testing on farms.SIGNIFICANCE: Most of the indicators selected had already been developed at the watershed scale, but attempting to adapt them to the farm scale was an originality. Farm-scale studies help understand sources of pollutant emissions that decrease water quality. Farmers’ use of comprehensive assessment tools would help encourage them to pursue their efforts in agroecological transition.

Existing studies typically explore the differences in seismic performance evaluation results of different evaluation models for concrete gravity dams by analyzing amplitude factors while neglecting the influence of ground motion duration. As a result, the influence of different evaluation models on the seismic performance of gravity dams can not be evaluated comprehensively. This paper, using the efficacy coefficient method, integrated the overall damage index of the dam body and the relative displacement of the dam crest to construct a comprehensive seismic damage evaluation model for gravity dams. Taking the Koyna gravity dam as the research subject, this paper examined the influence of ground motions with different strong seismic motion durations on the seismic damage degree of the dam body under both single-indicator and multi-indicator evaluation models, and the analysis results of different evaluation models on the seismic performance of gravity dams were compared. The result indicates that ground motion duration is positively correlated with the damage degree of gravity dams, and relying on a single evaluation indicator may lead to conservative or dangerous evaluations of the damage degree of dam bodies. A comprehensive evaluation model can more accurately reflect the influence of variations in strong seismic motion duration on the damage degree of concrete gravity dams.

Existing studies typically explore the differences in seismic performance evaluation results of different evaluation models for concrete gravity dams by analyzing amplitude factors while neglecting the influence of ground motion duration. As a result, the influence of different evaluation models on the seismic performance of gravity dams can not be evaluated comprehensively. This paper, using the efficacy coefficient method, integrated the overall damage index of the dam body and the relative displacement of the dam crest to construct a comprehensive seismic damage evaluation model for gravity dams. Taking the Koyna gravity dam as the research subject, this paper examined the influence of ground motions with different strong seismic motion durations on the seismic damage degree of the dam body under both single-indicator and multi-indicator evaluation models, and the analysis results of different evaluation models on the seismic performance of gravity dams were compared. The result indicates that ground motion duration is positively correlated with the damage degree of gravity dams, and relying on a single evaluation indicator may lead to conservative or dangerous evaluations of the damage degree of dam bodies. A comprehensive evaluation model can more accurately reflect the influence of variations in strong seismic motion duration on the damage degree of concrete gravity dams.

This study aims to develop an effective and reliable method for estimating rice quality indices and yield, addressing the growing need for rapid, non-destructive, and accurate predictions in modern agriculture. Field experiments were conducted in 2018 at the Suiling Water Conservancy Comprehensive Experimental Station (47°27′ N, 127°06′ E), using Longqingdao 3 as the test variety. Measurements included the leaf area index (LAI), chlorophyll content (SPAD), leaf nitrogen content (LNC), and leaf spectral reflectance during the tillering, jointing, and maturity stages. Based on these parameters, spectral indicators were calculated, and univariate linear regression models were developed to predict key rice quality indices. The results demonstrated that the optimal <i>R</i>² values for brown rice rate, moisture content, and taste value were 0.866, 0.913, and 0.651, with corresponding RMSE values of 0.122, 0.081, and 1.167. After optimizing the models, the <i>R</i>² values for the brown rice rate and taste value improved significantly to 0.95 (RMSE: 0.075) and 0.992 (RMSE: 0.179), respectively. Notably, the spectral index GM2 during the jointing stage achieved the highest accuracy for yield prediction, with an <i>R</i>² value of 0.822. These findings confirm that integrating multiple indicators across different growth periods enhances the accuracy of rice quality and yield predictions, offering a robust and intelligent solution for practical agricultural applications.

The variation in crop growth provides critical insights for yield estimation, crop health diagnosis, precision field management, and variable-rate fertilization. This study constructs key monitoring indicators (KMIs) for corn growth based on satellite remote sensing data, along with inversion models for these growth indicators. Initially, the leaf area index (LAI) and plant height were integrated into the KMI by calculating their respective weights using the entropy weight method. Eight vegetation indices derived from Sentinel-2A satellite remote sensing data were then selected: the Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI), Soil-Adjusted Vegetation Index (SAVI), Red-Edge Inflection Point (REIP), Inverted Red-Edge Chlorophyll Index (IRECI), Pigment Specific Simple Ratio (PSSRa), Terrestrial Chlorophyll Index (MTCI), and Modified Chlorophyll Absorption Ratio Index (MCARI). A comparative analysis was conducted to assess the correlation of these indices in estimating corn plant height and LAI. Through recursive feature elimination, the most highly correlated indices, REIP and IRECI, were selected as the optimal dual red-edge vegetation indices. A deep neural network (DNN) model was established for estimating corn plant height, achieving optimal performance with an R² of 0.978 and a root mean square error (RMSE) of 2.709. For LAI estimation, a DNN model optimized using particle swarm optimization (PSO) was developed, yielding an R² of 0.931 and an RMSE of 0.130. KMI enables farmers and agronomists to monitor crop growth more accurately and in real-time. Finally, this study calculated the KMI by integrating the inversion results for plant height and LAI, providing an effective framework for crop growth assessment using satellite remote sensing data. This successfully enables remote sensing-based growth monitoring for the 2023 experimental field in Haicheng, making the precise monitoring and management of crop growth possible.

Tomatoes (Solanum lycopersicum L.) represent a crucial fruit and vegetable crop whose leaf is a significant phenotypic parameter regulating photosynthesis and growth in a greenhouse environment. Chlorophyll content and leaf area index (LAI) are essential leaf indicators for directly monitoring alterations in tomato growth status. Therefore, this study conducted two years of experiments for collecting tomato growth parameters [tomato yield, vitamin C (VC), above-ground biomass (AGB)], sensors indicators [Chlorophyll content (SPAD), LAI], and image-based color indexes (CIs). Four machine learning methods and multiple-step regression (MSR) were applied to explore the monitoring model and methodology of the tomato growth parameters. The results demonstrated that leaf-based indicators performed well in nitrogen related indicators [leaf nitrogen content and N nutrition index (NNI)] and AGB estimation (|r|>0.7). CIs were important indicators in tomato yield and VC prediction (0.2<|r|<0.4). Machine learning methods could improve the prediction accuracy while decreasing the error based on the data of one type of sensor value (R2>0.31) or CIs (R2>0.31). The mixture of sensor values and CIs could predict the tomato indicators accurately (0.87>R2>0.42). MSR method measured a higher R2 and low error in tomato indicators (Linear: 0.81>R2>0.14, 19.56>RMSE>0.02; MSR: 0.85>R2>0.42, 10.73>RMSE>0.01). Machine learning obtained higher accuracy and lower error by combining sensors’ indexes and CIs for monitoring tomato growth indicators than the single indicator (sensor index or CI, 0.87>R2>0.45). Significantly, the least absolute shrinkage and selection operator measured the relatively higher R2 (>0.73) and lower error (RMSE<4.71, RE<5.46) on most tomato growth indicators’ prediction. Altogether, this study suggested that machine learning is promising in tomato indicator estimations of greenhouse crops, which contributed to developing a series of models (sensor value-based, CIs-based, and sensor value &amp; CIs-based) for quickly monitoring tomato crop growth and predicting yield.

This study addresses the deficiency in performance indicators of public institution arboretums and highlights the increasing need for collaborative cooperation for mutual growth. We aim to develop both quantitative and qualitative performance indicators to improve the evaluation systems and optimize operational efficiency. Initial indicators were formulated based on a review of 31 literature sources, followed by focus group interviews and a Delphi analysis involving 21 experts. A total of 23 core indicators were identified and categorized into three domains: public interest, economics, and functional aspects. Public interest indicators included seven items focusing on co-growth indicators (four) and contributions to the arboretum and garden culture development (three). Economic indicators featured ten items, including those related to regional exhibitions (four) and regional economic activation (six). The functional aspects indicators comprised six items: outsourced cultivation (three) and plant management (three). The highest-priority indicator was related to regional economic activity (income generation for local small business owners). Recommendations should be tailored to each department to bolster organizational performance, strengths, and capabilities. We anticipate that these indicators will support the mutual growth of national arboretums and local farming communities in Korea.

Abstract Background Acute-on-chronic liver failure (ACLF) is a severe clinical syndrome, and the incidence of invasive fungal infection (IFI) among hospitalized patients with ACLF is steadily increasing. The aim of this study is to develop a diagnostic nomogram to assist in the identification of IFI in these patients. Methods A retrospective study included 705 patients from January 1, 2019, to October 31, 2023, randomly divided into training (n = 493) and validation (n = 212) cohorts. The diagnosis of IFI includes proven diagnosis and probable diagnosis. Kaplan analysis was performed to analyze the survival prognosis of ACLF patients with and without IFI. A nomogram was developed based on a logistic regression model derived through least absolute shrinkage and selection operator (LASSO) regression. The discrimination, accuracy, and clinical utility of the model were assessed using receiver operating characteristic curves, Hosmer-Lemeshow tests, calibration plots, and decision curve analysis. Results Kaplan–Meier survival analysis confirmed that the median survival time of ACLF patients with IFI was significantly lower (by 68 days) than that of ACLF patients without IFI, and there were significant differences in the 90-day, 180-day, and 360-day survival rates between the two groups (P < 0.05). Based on LASSO regression, the following factors were identified as significant risk factors for predicting IFI: aminotransferase levels, prothrombin activity, hemoglobin, neutrophil-to-lymphocyte ratio, and serum total bilirubin. A nomogram was constructed incorporating these variables. The nomogram demonstrated good discriminative ability, with an area under the receiver operating characteristic curve (AUC) of 0.78 (95% confidence interval [CI]: 0.72–0.84) in the training cohort and 0.79 (95% CI: 0.70–0.87) in the validation cohort. Decision curve analysis further validated the clinical applicability of the nomogram. Conclusion ACLF patients with IFI have lower survival time than those without IFI. A nomogram was developed and validated to assist clinicians in the early prediction of IFI in hospitalized patients with ACLF. Clinical trial number Not applicable.

This study reports on the evolution over a 15-year period and the current state of the European Union’s food and drink industry. Furthermore, it assesses EU food industry competitiveness benchmarked against the US, UK, China, and Canada. The assessment is based on trade indicators (relative comparative advantage and export share of the world market) and other economic indicators (value added, labour productivity, and share value added in total Manufacturing).

Quantifiable derivation of soil quality is prerequisite for the sustainability of cropping system. The experiment was purposively done in 2018 to identify the most appropriate soil quality indicators and to execute the impact of three most prevalent cropping systems (rice-rice, rice-potato-jute and rice-potatomaize) on soil quality in New Alluvial Zone of West Bengal, India. We have collected 72 soil samples (20 cm depth) after completion of 3rd year cropping cycle and analyzed them for seventeen (17) soil attributes including physical, chemical, and biological parameters. Principal components Analysis (PCA) were performed to formulate minimum dataset (MDS) i.e. the key indicators. SQI was formulated based on the quantitative values of the key indicators for the three rice-based cropping systems. The key indicators found for these Indo-Gangetic soils of West Bengal under rice-based cropping system were: organic carbon (%), available potassium, DTPA Fe, DTPA Mn, DTPA Cu, AS (%), MBC and dehydrogenase activity. In rice-rice cropping system (CS1) the soil quality parameters were low (3.20) and are not desirable for soil health and should be prevented for long term cultivation. However, in rice-potato-jute cropping system (CS2), it was found as SQI 3.34 whereas in CS3, it was 3.22. Therefore, CS2 cropping system can be adopted for long term rather than CS1 and CS3 for the betterment of soil quality in this region.