A new digital technology to reduce fungicide use in vineyards.

There is a growing demand for technologies able to decrease the environmental impact of agricultural activities without penalizing quali-quantitative characteristics of productions. In the case of viticulture, one of the key problems is represented by the spray drift during fungicide treatments. The diffusion in operational farming contexts of technologies based on variable-rate and recycling tunnel sprayers is often limited by their cost and, for the latter, by their size and lower maneuverability, representing clear disadvantages especially in case of small farms or in hilly and mountain areas. We present a new digital technology implemented in a mobile app that supports the reduction of both the number of treatments and the amount of fungicide distributed per treatment. The technology is based (i) on an alert system that prevents unneeded treatments in case of no risk of infection and (ii) on the quantification of the optimal amounts of active ingredients and dilution water based on the sprayer type/settings and on leaf area index values estimated with a common smartphone. An internal database allows to adjust (in case of need) the active ingredient dose to assure full compliance with product's legal requirements. In case of heterogeneity in leaf area index values inside the vineyard, prescription maps are generated. Results from a 2-year case study in a vineyard in northern Italy are shown, where the system allowed to reduce by 26.4 % and 27.4 % (mean of two years), respectively, the seasonal amounts of fungicides and dilution water, and by 43.8 % the copper content in must. The high usability of the technology proposed (just a common smartphone is needed) and the fact that it does not require updating the farm machine park highlights the suitability of the proposed solution for operational farming conditions, including premium wine production districts often characterized by small farms in hilly areas.

A trait-based model ensemble approach to design rice plant types for future climate.

Crop models are powerful tools to support breeding because of their capability to explore genotype × environment×management interactions that can help design promising plant types under climate change. However, relationships between plant traits and model parameters are often model specific and not necessarily direct, depending on how models formulate plant morphological and physiological features. This hinders model application in plant breeding. We developed a novel trait-based multi-model ensemble approach to improve the design of rice plant types for future climate projections. We conducted multi-model simulations targeting enhanced productivity, and aggregated results into model-ensemble sets of phenotypic traits as defined by breeders rather than by model parameters. This allowed to overcome the limitations due to ambiguities in trait-parameter mapping from single modelling approaches. Breeders' knowledge and perspective were integrated to provide clear mapping from designed plant types to breeding traits. Nine crop models from the AgMIP-Rice Project and sensitivity analysis techniques were used to explore trait responses under different climate and management scenarios at four sites. The method demonstrated the potential of yield improvement that ranged from 15.8% to 41.5% compared to the current cultivars under mid-century climate projections. These results highlight the primary role of phenological traits to improve crop adaptation to climate change, as well as traits involved with canopy development and structure. The variability of plant types derived with different models supported model ensembles to handle related uncertainty. Nevertheless, the models agreed in capturing the effect of the heterogeneity in climate conditions across sites on key traits, highlighting the need for context-specific breeding programmes to improve crop adaptation to climate change. Although further improvement is needed for crop models to fully support breeding programmes, a trait-based ensemble approach represents a major step towards the integration of crop modelling and breeding to address climate change challenges and develop adaptation options.

Setting-up of different water managements as mitigation strategy of the environmental impact of paddy rice.

Northern Italy represents the most important rice-growing district in Europe. In this area, rice is the main annual crop and the main revenues source for farmers. However, Italian climatic condition led to a traditional cultivation characterized by continuous flooding, causing emissions of methane into the atmosphere due to the organic matter fermentation in anaerobic conditions, and, consequently, a high environmental impact. The water conditions of paddy fields also affect heavy metals uptake by rice plants. In this context, this study focuses on the evaluation of environmental impact and of heavy metal content in paddy rice, and it may represent an important step in mitigating the environmental impact of rice production. In detail, this study quantifies the environmental benefits related to the adoption of an alternative water management characterized by an additional aeration period during stem elongation. To this purpose, field trials were carried out and the Life Cycle Assessment (LCA) approach was applied with a cradle-to-farm gate perspective. The potential environmental impact of the production of two rice varieties (Carnaroli and Caravaggio) was analysed in terms of 12 different impact categories and dehulled rice grain were analysed for arsenic and cadmium content. Alternative flooding decreases CH4 emissions in all cases evaluated (from 15% to 52%), resulting in a reduction in the climate change impact of rice cultivation (from 12% to 32%). Furthermore, the alternative water management does not influence grain yield and it reduces all the other environmental impact categories in 2 out of 4 cases. Regarding the heavy metals contents, the arsenic content in the grain decreases in all alternative scenarios, whereas the cadmium content increases, while remaining well below the legal limits.

May smart technologies reduce the environmental impact of nitrogen fertilization? A case study for paddy rice.

Precision agriculture is increasingly considered as a powerful solution to mitigate the environmental impact of farming systems. This is because of its ability to use multi-source information in decision support systems to increase the efficiency of farm management. Among the agronomic practices for which precision agriculture concepts were applied in research and operational contexts, variable rate (VR) nitrogen fertilization plays a key role. A promising approach to make quantitative, spatially distributed diagnoses to support VR N fertilization is based on the combined use of remote sensing information and few smart scouting-driven ground estimates to derive maps of nitrogen nutrition index (NNI). In this study, a new smart app for field NNI estimates (PocketNNI) was developed, which can be integrated with remote sensing data. The environmental impact of using PocketNNI and Sentinel 2 products to drive fertilization was evaluated using the Life Cycle Assessment approach and a case study on rice in northern Italy. In particular, the environmental performances of rice fertilized according to VR information derived from the integration of PocketNNI and satellite data was compared with a treatment based on uniform N application. Primary data regarding the cultivation practices and the achieved yields were collected during field tests. Results showed that VR fertilization allowed reducing the environmental impact by 11.0% to 13.6% as compared to uniform N application. For Climate Change, the impact is reduced from 937.3 to 832.7 kg CO2 eq/t of paddy rice. The highest environmental benefits - mainly due to an improved ratio between grain yield and N fertilizers - were achieved in terms of energy consumption for fertilizer production and of emission of N compounds. Although further validation is needed, these preliminary results are promising and provide a first quantitative indication of the environmental benefits that can be achieved when digital technologies are used to support N fertilization.

Tailoring parameter distributions to specific germplasm: impact on crop model-based ideotyping.

Crop models are increasingly used to identify promising ideotypes for given environmental and management conditions. However, uncertainty must be properly managed to maximize the in vivo realizability of ideotypes. We focused on the impact of adopting germplasm-specific distributions while exploring potential combinations of traits. A field experiment was conducted on 43 Italian rice varieties representative of the Italian rice germplasm, where the following traits were measured: light extinction coefficient, radiation use efficiency, specific leaf area at emergence and tillering. Data were used to derive germplasm-specific distributions, which were used to re-run a previous modelling experiment aimed at identifying optimal combinations of plant trait values. The analysis, performed using the rice model WARM and sensitivity analysis techniques, was conducted under current conditions and climate change scenarios. Results revealed that the adoption of germplasm-specific distributions may markedly affect ideotyping, especially for the identification of most promising traits. A re-ranking of some of the most relevant parameters was observed (radiation use efficiency shifted from 4th to 1st), without clear relationships between changes in rankings and differences in distributions for single traits. Ideotype profiles (i.e., values of the ideotype traits) were instead more consistent, although differences in trait values were found.

Predicting rice blast disease: machine learning versus process-based models.

BACKGROUND: In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and Water Accounting Rice Model (WARM)) and two approaches based on machine learning algorithms (M5Rules and Recurrent Neural Networks (RNN)), the former inducing a rule-based model and the latter building a neural network. In situ telemetry is important to obtain quality in-field data for predictive models and this was a key aspect of the RICE-GUARD project on which this study is based. According to the authors, this is the first time process-based and machine learning modelling approaches for supporting plant disease management are compared. RESULTS: Results clearly showed that the models succeeded in providing a warning of rice blast onset and presence, thus representing suitable solutions for preventive remedial actions targeting the mitigation of yield losses and the reduction of fungicide use. All methods gave significant "signals" during the "early warning" period, with a similar level of performance. M5Rules and WARM gave the maximum average normalized scores of 0.80 and 0.77, respectively, whereas Yoshino gave the best score for one site (Kalochori 2015). The best average values of r and r2 and %MAE (Mean Absolute Error) for the machine learning models were 0.70, 0.50 and 0.75, respectively and for the process-based models the corresponding values were 0.59, 0.40 and 0.82. Thus it has been found that the ML models are competitive with the process-based models. This result has relevant implications for the operational use of the models, since most of the available studies are limited to the analysis of the relationship between the model outputs and the incidence of rice blast. Results also showed that machine learning methods approximated the performances of two process-based models used for years in operational contexts. CONCLUSIONS: Process-based and data-driven models can be used to provide early warnings to anticipate rice blast and detect its presence, thus supporting fungicide applications. Data-driven models derived from machine learning methods are a viable alternative to process-based approaches and - in cases when training datasets are available - offer a potentially greater adaptability to new contexts.

Quantifying Uncertainty Due to Stochastic Weather Generators in Climate Change Impact Studies.

Climate change studies involve complex processes translating coarse climate change projections in locally meaningful terms. We analysed the behaviour of weather generators while downscaling precipitation and air temperature data. With multiple climate indices and alternative weather generators, we directly quantified the uncertainty associated with using weather generators when site specific downscaling is performed. We extracted the influence of weather generators on climate variability at local scale and the uncertainty that could affect impact assessment. For that, we first designed the downscaling experiments with three weather generators (CLIMAK, LARS-WG, WeaGETS) to interpret future projections. Then we assessed the impacts of estimated changes of precipitation and air temperature for a sample of 15 sites worldwide using a rice yield model and an extended set of climate metrics. We demonstrated that the choice of a weather generator in the downscaling process may have a higher impact on crop yield estimates than the climate scenario adopted. Should they be confirmed, these results would indicate that widely accepted outcomes of climate change studies using this downscaling technique need reconsideration.

Estimating Crop Nutritional Status Using Smart Apps to Support Nitrogen Fertilization. A Case Study on Paddy Rice.

Accurate nitrogen (N) management is crucial for the economic and environmental sustainability of cropping systems. Different methods have been developed to increase the efficiency of N fertilizations. However, their costs and/or low usability have often prevented their adoption in operational contexts. We developed a diagnostic system to support topdressing N fertilization based on the use of smart apps to derive a N nutritional index (NNI; actual/critical plant N content). The system was tested on paddy rice via dedicated field experiments, where the smart apps PocketLAI and PocketN were used to estimate, respectively, critical (from leaf area index) and actual plant N content. Results highlighted the system's capability to correctly detect the conditions of N stress (NNI < 1) and N surplus (NNI > 1), thereby effectively supporting topdressing fertilizations. A resource-efficient methodology to derive PocketN calibration curves for different varieties-needed to extend the system to new contexts-was also developed and successfully evaluated on 43 widely grown European varieties. The widespread availability of smartphones and the possibility to integrate NNI and remote sensing technologies to derive variable rate fertilization maps generate new opportunities for supporting N management under real farming conditions.

Quantifying the Accuracy of Digital Hemispherical Photography for Leaf Area Index Estimates on Broad-Leaved Tree Species.

Digital hemispherical photography (DHP) has been widely used to estimate leaf area index (LAI) in forestry. Despite the advancement in the processing of hemispherical images with dedicated tools, several steps are still manual and thus easily affected by user's experience and sensibility. The purpose of this study was to quantify the impact of user's subjectivity on DHP LAI estimates for broad-leaved woody canopies using the software Can-Eye. Following the ISO 5725 protocol, we quantified the repeatability and reproducibility of the method, thus defining its precision for a wide range of broad-leaved canopies markedly differing for their structure. To get a complete evaluation of the method accuracy, we also quantified its trueness using artificial canopy images with known canopy cover. Moreover, the effect of the segmentation method was analysed. The best results for precision (restrained limits of repeatability and reproducibility) were obtained for high LAI values (>5) with limits corresponding to a variation of 22% in the estimated LAI values. Poorer results were obtained for medium and low LAI values, with a variation of the estimated LAI values that exceeded the 40%. Regardless of the LAI range explored, satisfactory results were achieved for trees in row-structured plantations (limits almost equal to the 30% of the estimated LAI). Satisfactory results were achieved for trueness, regardless of the canopy structure. The paired t-test revealed that the effect of the segmentation method on LAI estimates was significant. Despite a non-negligible user effect, the accuracy metrics for DHP are consistent with those determined for other indirect methods for LAI estimates, confirming the overall reliability of DHP in broad-leaved woody canopies.

Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments.

The CO2 fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO2] (E-[CO2]) by comparison to free-air CO2 enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well. However, yield prediction in response to E-[CO2] varied significantly among the rice models. The variation was not random: models that overestimated at one experiment simulated greater yield enhancements at the others. The variation was not associated with model structure or magnitude of photosynthetic response to E-[CO2] but was significantly associated with the predictions of leaf area. This suggests that modelled secondary effects of E-[CO2] on morphological development, primarily leaf area, are the sources of model uncertainty. Rice morphological development is conservative to carbon acquisition. Uncertainty will be reduced by incorporating this conservative nature of the morphological response to E-[CO2] into the models. Nitrogen levels, particularly under limited situations, make the prediction more uncertain. Improving models to account for [CO2] × N interactions is necessary to better evaluate management practices under climate change.

Trait-based model development to support breeding programs. A case study for salt tolerance and rice.

Eco-physiological models are increasingly used to analyze G × E × M interactions to support breeding programs via the design of ideotypes for specific contexts. However, available crop models are only partly suitable for this purpose, since they often lack clear relationships between parameters and traits breeders are working on. Taking salt stress tolerance and rice as a case study, we propose a paradigm shift towards the building of ideotyping-specific models explicitly around traits involved in breeding programs. Salt tolerance is a complex trait relying on different physiological processes that can be alternatively selected to improve the overall crop tolerance. We developed a new model explicitly accounting for these traits and we evaluated its performance using data from growth chamber experiments (e.g., R2 ranged from 0.74 to 0.94 for the biomass of different plant organs). Using the model, we were able to show how an increase in the overall tolerance can derive from completely different physiological mechanisms according to soil/water salinity dynamics. The study demonstrated that a trait-based approach can increase the usefulness of mathematical models for supporting breeding programs.

Surfing parameter hyperspaces under climate change scenarios to design future rice ideotypes.

Growing food crops to meet global demand and the search for more sustainable cropping systems are increasing the need for new cultivars in key production areas. This study presents the identification of rice traits putatively producing the largest yield benefits in five areas that markedly differ in terms of environmental conditions in the Philippines, India, China, Japan and Italy. The ecophysiological model WARM and sensitivity analysis techniques were used to evaluate phenotypic traits involved with light interception, photosynthetic efficiency, tolerance to abiotic stressors, resistance to fungal pathogens and grain quality. The analysis involved only model parameters that have a close relationship with phenotypic traits breeders are working on, to increase the in vivo feasibility of selected ideotypes. Current climate and future projections were considered, in the light of the resources required by breeding programs and of the role of weather variables in the identification of promising traits. Results suggest that breeding for traits involved with disease resistance, and tolerance to cold- and heat-induced spikelet sterility could provide benefits similar to those obtained from the improvement of traits involved with canopy structure and photosynthetic efficiency. In contrast, potential benefits deriving from improved grain quality traits are restricted by weather variability and markedly affected by G × E interactions. For this reason, district-specific ideotypes were identified using a new index accounting for both their productivity and feasibility.

Estimating Leaf Area Index (LAI) in Vineyards Using the PocketLAI Smart-App.

Estimating leaf area index (LAI) of Vitis vinifera using indirect methods involves some critical issues, related to its discontinuous and non-homogeneous canopy. This study evaluates the smart app PocketLAI and hemispherical photography in vineyards against destructive LAI measurements. Data were collected during six surveys in an experimental site characterized by a high level of heterogeneity among plants, allowing us to explore a wide range of LAI values. During the last survey, the possibility to combine remote sensing data and in-situ PocketLAI estimates (smart scouting) was evaluated. Results showed a good agreement between PocketLAI data and direct measurements, especially for LAI ranging from 0.13 to 1.41 (R² = 0.94, RRMSE = 17.27%), whereas the accuracy decreased when an outlying value (vineyard LAI = 2.84) was included (R² = 0.77, RRMSE = 43.00%), due to the saturation effect in case of very dense canopies arising from lack of green pruning. The hemispherical photography showed very high values of R², even in presence of the outlying value (R² = 0.94), although it showed a marked and quite constant overestimation error (RRMSE = 99.46%), suggesting the need to introduce a correction factor specific for vineyards. During the smart scouting, PocketLAI showed its reliability to monitor the spatial-temporal variability of vine vigor in cordon-trained systems, and showed a potential for a wide range of applications, also in combination with remote sensing.

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions.

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2 ]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2 ] and temperature.

Estimating biogas production of biologically treated municipal solid waste.

In this work, a respirometric approach, i.e., Dynamic Respiration Index (DRI), was used to predict the anaerobic biogas potential (ABP), studying 46 waste samples coming directly from MBT full-scale plants. A significant linear regression model was obtained by a jackknife approach: ABP=(34.4+/-2.5)+(0.109+/-0.003).DRI. The comparison of the model of this work with those of the previous works using a different respirometric approach (Sapromat-AT(4)), allowed obtaining similar results and carrying out direct comparison of different limits to accept treated waste in landfill, proposed in the literature. The results indicated that on an average, MBT treatment allowed 56% of ABP reduction after 4weeks of treatment, and 79% reduction after 12weeks of treatment. The obtainment of another regression model allowed transforming Sapromat-AT(4) limit in DRI units, and achieving a description of the kinetics of DRI and the corresponding ABP reductions vs. MBT treatment-time.

The -308 (G/A) single nucleotide polymorphism in the TNF-alpha gene and the risk of major depression in the elderly.

OBJECTIVE: The immune system (IS) plays a key role in the mechanisms underlying major depression (MD) and pro-inflammatory cytokines seem to be particularly involved in the pathogenesis of the disease. There is growing evidence of a relationship between commonly studied single nucleotide polymorphisms (SNPs) in cytokine genes and an increased risk of MD.The aim of our study was to investigate the association between the -308(G/A) SNP in the tumour necrosis factor-alpha (TNF-alpha) gene and late-life MD in elderly people without dementia. METHODS: Blood samples were obtained from 50 subjects enrolled at the Geriatric Department of the San Gerardo Hospital in Monza, Italy, after screening with the geriatric depression scale (GDS > or = 15) and mini-mental state evaluation (MMSE > or = 24). The -308 (G/A) SNP was genotyped by SSP-PCR amplification. Two hundred and forty age-matched healthy volunteers were taken as the control group. RESULTS: Genotype and allele distributions in patients with MD were significantly different from those of the controls. In subjects affected by MD we found a higher percentage of the GG genotype (84 vs. 68,3%; p = 0.007) and thus of the G allele (92 vs. 81,9%; p = 0.05).The GG genotype was associated with a greater risk of developing the disease (OR 2.433, CI 1.09-5.43). CONCLUSIONS: Our study suggests that the -308 (G/A) polymorphism in the TNF-alpha gene could play a role in determining susceptibility to MD. An activation of the TNF-alpha system could contribute to the development of MD in the elderly.

Analytical Method Performance Evaluation (AMPE)--a software tool for analytical method validation.

A Windows-based software tool [Analytical Method Performance Evaluation (AMPE)] was developed to support the validation of analytical methods. The software implements standard statistical approaches commonly adopted in validation studies to estimate analytical method performance (limits of detection and quantitation, accuracy, specificity, working range, and linearity of responses) according to ISO 5725. In addition, AMPE proposes the application of innovative and unique approaches for the assessment of analytical method performance. Specifically, AMPE proposes the use of difference-based indexes to quantify the agreement between measurements and reference values, the use of pattern indexes to quantify methods bias with respect to specific external variables, and the application of fuzzy logic to aggregate into synthetic indicators the information collected independently via the different performance statistics traditionally estimated in validation studies. Aggregated measures are particularly useful for methods comparison, when more than one method is available for a specific analysis and it may be of interest to identify the best performing one taking into account, simultaneously, the information available from different performance statistics. Illustrative examples of the type of outputs expected from AMPE-based validation sessions are given. The extensive data handling capabilities and the wide range of statistics supplied in the software package makes AMPE suitable for specific needs that may arise in different validation studies. The installation package, complete with a fully documented help file, is distributed free of charge to interested users along with input files exemplary of the type of entry data required to run validation data analyses.

Multicenter study on hepatitis C virus-related cryoglobulinemic glomerulonephritis.

BACKGROUND: Mixed cryoglobulinemia is a multisystem disorder associated strongly with hepatitis C virus (HCV) infection. The kidney frequently is involved, and glomerulonephritis represents the key factor affecting prognosis. METHODS: Clinical, serological, immunogenetic, and morphological data were collected retrospectively from medical records of 146 patients with cryoglobulinemic glomerulonephritis who underwent biopsies in 25 Italian centers and 34 cryoglobulinemic controls without renal involvement. RESULTS: Eighty-seven percent of patients were infected with HCV; genotype 1b was more frequent than genotype 2 (55% versus 43%). Diffuse membranoproliferative glomerulonephritis was the most prevalent histological pattern (83%). Type II cryoglobulin (immunoglobulin Mkappa [IgMkappa]/IgG) was detected in 74.4% of cases. The remainder had type III (polyclonal IgM/IgG) cryoglobulins. A multivariate Cox proportional hazard model showed that age, serum creatinine level, and proteinuria at the onset of renal disease were associated independently with risk for developing severe renal failure at follow-up. Overall survival at 10 years was about 80%. Kaplan-Meier survival curves were worsened by a basal creatinine value greater than 1.5 mg/dL (>133 mumol/L), but were unaffected by sex and HCV infection. Cardiovascular disease was the cause of death in more than 60% of patients. CONCLUSION: Data confirm the close association between mixed cryoglobulinemia and HCV infection and between glomerulonephritis and type II cryoglobulin. Survival profiles are better than previously reported in the literature, probably because of improvement in therapeutic regimens. Causes of death reflect this improvement in survival, with an increased prevalence of cardiovascular events compared with infectious complications and hepatic failure, which were predominant in the past.

Dynamic respiration index as a descriptor of the biological stability of organic wastes.

Analytical methods applicable to different organic wastes are needed to establish the extent to which readily biodegradable organic matter has decomposed (i.e., biological stability). The objective of this study was to test a new respirometric method for biological stability determination of organic wastes. Dynamic respiration index (DRI) measurements were performed on 16 organic wastes of different origin, composition, and biological stability degree to validate the test method and result expression, and to propose biological stability limits. In addition, theoretical DRI trends were obtained by using a mathematical model. Each test lasted 96 h in a 148-L-capacity respirometer apparatus, and DRI was monitored every hour. The biological stability was expressed as both single and cumulative DRI values. Results obtained indicated that DRI described biological stability in relation to waste typology and age well, revealing lower-stability waste characterized by a well-pronounced DRI profile (a marked peak was evident) that became practically flat for samples with higher biological stability. Fitting indices showed good model prediction compared with the experimental data, indicating that the method was able to reproduce the aerobic process, providing a reliable indication of the biological stability. The DRI can therefore be proposed as a useful method to measure the biological stability of organic wastes, and DRI values, calculated as a mean of 24 h of the highest microbial activity, of 1000 and 500 mg O(2) kg(-1) volatile solids (VS) h(-1) are proposed to indicate medium (e.g., fresh compost) and high (e.g., mature compost) biological stabilities, respectively.