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Flow or collective movement is a frequently observed phenomenon for many cellular components including the cytoskeletal proteins actin and myosin. To study protein flow in living cells, we and others have previously used spatiotemporal image correlation spectroscopy (STICS) analysis on fluorescence microscopy image time series. Yet, in cells, multiple protein flows often occur simultaneously on different scales resulting in superimposed fluorescence intensity fluctuations that are challenging to separate using STICS. Here, we exploited the characteristic that distinct protein flows often occur at different spatial scales present in the image series to disentangle superimposed protein flow dynamics. We employed a newly developed and an established spatial filtering algorithm to alternatively accentuate or attenuate local image intensity heterogeneity across different spatial scales. Subsequently, we analysed the spatially filtered time series with STICS, allowing the quantification of two distinct superimposed flows within the image time series. As a proof of principle of our analysis approach, we used simulated fluorescence intensity fluctuations as well as time series of nonmuscle myosin II in endothelial cells and actin‐based podosomes in dendritic cells and revealed simultaneously occurring contiguous and noncontiguous flow dynamics in each of these systems. Altogether, this work extends the application of STICS for the quantification of multiple protein flow dynamics in complex biological systems including the actomyosin cytoskeleton. [ABSTRACT FROM AUTHOR]

Spring barley (Hordeum vulgare L.) is an increasingly important cash crop in the province of Quebec (Canada). Soil–crop models are powerful tools for analyzing and supporting sustainable crop production. STICS model has not yet been tested for spring barley grown over several decades. This study was conducted to calibrate and evaluate the STICS model, without annual reinitialization, for predicting aboveground biomass and N nutrition attributes at harvest during 31 years of successive cropping of spring barley grown in soil (silty clay, Humic Gleysol) from the Saguenay–Lac-Saint-Jean region (northeastern Quebec, Canada). There is a good agreement between observed and predicted variables during the 31 successive barley cropping years. STICS predicted well biomass accumulation and plant N content with a low relative bias (|normalized mean error| = 0–13%) and small prediction error (normalized root mean square error = 6–25%). Overall, the STICS outputs reproduced the same trends as the field-observed data with various tillage systems and N sources. Predictions of crop attributes were more accurate in years with rainfall close to the long-term average. These 'newly calibrated' parameters in STICS for spring barley cropped under continental cold and humid climates require validation using independent observation datasets from other sites. [ABSTRACT FROM AUTHOR]

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Traditional forms of community supervision focusing on control and punitive functions have been shown to be ineffective in improving client outcomes. In response, several officer training programs, including the Strategic Training Initiative in Community Supervision (STICS), Effective Practices in Community Supervision (EPICS), and Staff Training Aimed at Reducing Re-arrest (STARR) models, have been developed to better incorporate more rehabilitative-focused strategies into community corrections practices. In this meta-analysis of 25 studies, we assessed the impact of these programs on a variety of officer and client outcomes. Findings revealed that officer training increases the focus of the discussion content and use of core correctional practice skills during interactions with clients. Results also indicated that training in these programs produces reductions in client recidivism, especially among officers who implement core correctional practice skills with greater fidelity. This study supports the continued use of officer training programs and identifies areas for future research. [ABSTRACT FROM AUTHOR]

The premier paradigm of offender rehabilitation is the Risk-Need-Responsivity (RNR) model and several training programs for community corrections officers to better apply the RNR principles have been developed. This article summarizes and critiques three major training models: STICS, EPICS, and STARR. Furthermore, lessons are drawn from the research to guide further improvements in the training of community correctional staff. The community supervision of persons in conflict with the law is one of the most widely used correctional sanctions in North America and yet, its effectiveness in reducing recidivism is questionable. The review of training programs that capitalize on the RNR model shows that community supervision can lead to changes in officer behavior and reduced recidivism. However, to achieve promising results modifications to the training and to the agencies that adopt such training programs are necessary. [ABSTRACT FROM AUTHOR]

Spring barley (Hordeum vulgare L.) is an increasingly important cash crop in the province of Quebec (Canada). Soil–crop models are powerful tools for analyzing and supporting sustainable crop production. STICS model has not yet been tested for spring barley grown over several decades. This study was conducted to calibrate and evaluate the STICS model, without annual reinitialization, for predicting aboveground biomass and N nutrition attributes at harvest during 31 years of successive cropping of spring barley grown in soil (silty clay, Humic Gleysol) from the Saguenay–Lac-Saint-Jean region (northeastern Quebec, Canada). There is a good agreement between observed and predicted variables during the 31 successive barley cropping years. STICS predicted well biomass accumulation and plant N content with a low relative bias (|normalized mean error| = 0–13%) and small prediction error (normalized root mean square error = 6–25%). Overall, the STICS outputs reproduced the same trends as the field-observed data with various tillage systems and N sources. Predictions of crop attributes were more accurate in years with rainfall close to the long-term average. These ‘newly calibrated’ parameters in STICS for spring barley cropped under continental cold and humid climates require validation using independent observation datasets from other sites.

The Fallopian tube epithelium harbors the origin cells for the majority of high-grade serous ovarian carcinomas (HGSCs), the most lethal form of gynecologic malignancies. PAX8 belongs to the paired-box gene family of transcription factors and it is a marker of the FTE secretory cell lineage. Its role has been investigated in migration, invasion, proliferation, cell survival, stem cell maintenance, angiogenesis and tumor growth. In this review, we focus on the pro-tumorigenic role of PAX8 in ovarian cancer; in this context, PAX8 possibly continues to exert its transcriptional activity on its physiological targets but may also function on newly available targets after the tumorigenic hits. Acquiring new insights into the different PAX8 mechanism(s) of action in the tumor microenvironment could uncover new viable therapeutic targets and thus improve the current treatment regimen. [ABSTRACT FROM AUTHOR]

Intercropping of two or more species on the same piece of land can enhance biodiversity and resource use efficiency in agriculture. Traditionally, intercropping systems have been developed and improved by empirical methods within a specific local context. To support the development of promising intercropping systems, the individual species that are part of an intercrop can be subjected to breeding. Breeding for intercropping aims at resource foraging traits of the admixed species to maximize niche complementarity, niche facilitation, and intercrop performance. The breeding process can be facilitated by modeling tools that simulate the outcome of the combination of different species' (or genotypes') traits for growth and yield development, reducing the need of extensive field testing. Here, we revisit the challenges associated with breeding for intercropping, and give an outlook on applying crop growth models to assist breeding for intercropping. We conclude that crop growth models can assist breeding for intercropping, provided that (i) they incorporate the relevant plant features and mechanisms driving interspecific plant–plant interactions; (ii) they are based on model parameters that are closely linked to the traits that breeders would select for; and (iii) model calibration and validation is done with field data measured in intercrops. Minimalist crop growth models are more likely to incorporate the above elements than comprehensive but parameter-intensive crop growth models. Their lower complexity and reduced parameter requirement facilitate the exploration of mechanisms at play and fulfil the model requirements for calibration of the appropriate crop growth models. [ABSTRACT FROM AUTHOR]

Intercropping of two or more species on the same piece of land can enhance biodiversity and resource use efficiency in agriculture. Traditionally, intercropping systems have been developed and improved by empirical methods within a specific local context. To support the development of promising intercropping systems, the individual species that are part of an intercrop can be subjected to breeding. Breeding for intercropping aims at resource foraging traits of the admixed species to maximize niche complementarity, niche facilitation, and intercrop performance. The breeding process can be facilitated by modeling tools that simulate the outcome of the combination of different species’ (or genotypes’) traits for growth and yield development, reducing the need of extensive field testing. Here, we revisit the challenges associated with breeding for intercropping, and give an outlook on applying crop growth models to assist breeding for intercropping. We conclude that crop growth models can assist breeding for intercropping, provided that (i) they incorporate the relevant plant features and mechanisms driving interspecific plant–plant interactions; (ii) they are based on model parameters that are closely linked to the traits that breeders would select for; and (iii) model calibration and validation is done with field data measured in intercrops. Minimalist crop growth models are more likely to incorporate the above elements than comprehensive but parameter-intensive crop growth models. Their lower complexity and reduced parameter requirement facilitate the exploration of mechanisms at play and fulfil the model requirements for calibration of the appropriate crop growth models.

Soil is a reservoir of natural capital that provides several ecosystem services, ensuring human well-being and sustainable socioeconomic development. Many researchers nevertheless argue that there is no consensus on practical indicators to assess soil ecosystem services (SES). As many policy decisions rely on metrics and indicators to communicate concise and relevant information, an assessment of ecosystem service indicators can help identifying gaps hindering policymakers from more fully adopting ecosystem service approaches. The aim of this study was to develop a method to quantitatively evaluate six SES using a set of indicators derived from dynamic soil and crop modelling using the STICS model developed by INRA. In a 6 775 km2 study area in north-western France (Brittany), 64 soil sampling points, located in agricultural areas, were selected following a stratified random sampling design based on soil parent material stratification. STICS inputs required climate data, soil property data and soil and landscape management practices. Over a baseline period from 1988 to 2018, similar crop management practices were simulated that reflected the dominant one performed by conventional farmers across the study site: applying organic and mineral fertilizers to a maize-wheat-catch crop rotation. Also, STICS outputs were used to derive six biophysical indicators characterizing six SES. Both mean and annual SES indicators were calculated and analyzed. Interrelations among SES indicators and soil properties were investigated using a Pearson correlation matrix, multivariate variance analysis and regression-based methods. The main results revealed that pedological and inter-annual variability were the main drivers of SES provision, particularly for ground water recharge, plant biomass provision, plant water provision and carbon sequestration. All SES were strongly correlated, except carbon sequestration and plant nitrogen provision, which showed weak correlations with the rest of SES. Moreover, analyzing interrelations between SES indicators and soil properties such as soil depth, soil texture and its related variables such as available water capacity played a predominant role in ensuring high levels of water-related SES indicators. Meanwhile, physicochemical soil properties were strongly correlated with carbon sequestration but less so with plant nitrogen provision. Overall, these findings characterize the effect of soil variability on SES provision using modelling and field measures of several soil properties, which can be useful for policy makers.

Repeated applications of organic amendments increase soil organic carbon (SOC) storage and nitrogen (N) availability for crops. Soil-crop models can facilitate the study of these effects and optimize the management of amendments. In soil-crop models, C and N mineralization of amendments is simulated by several pools of soil organic matter which have their specific turnover rate. We used the STICS model to quantify the effects of amendments on C and N dynamics in the long-term QualiAgro experiment, in which four amendments were spread every 2 years since 1998. We studied the model's ability to simulate laboratory incubation and field experiments, depending on the calibration method and the partitioning of the amendment into one or two pools. The one-pool model simulated C and N dynamics in the field experiment as accurately as the two-pools model with a calibration based on field data. However, C and N dynamics measured under laboratory conditions could only be simulated with the two-pools model, which was then used to simulate C and N in the field. The root mean square errors were 1.6 t DM ha−1 for grain yield, 2.6 t C ha−1 for SOC and 41 kg N ha−1 for soil mineral N. Model parameters could be determined using the C:N ratio of amendments and the indicator of remaining organic carbon (IROC), measured at the laboratory. The STICS model can thus be used to simulate SOC and N dynamics with long-term amendments with a simple calibration. [ABSTRACT FROM AUTHOR]

In the World Heritage Côa region, in northern Portugal, agriculture has crucial economic, social and cultural importance. Vineyards and olive groves are part of the economic base of this region, contributing to the regional commercial budget and the livelihood of its residents. Climate change is expected to have significant impacts on these crops, where climatic conditions are already very warm and dry, posing a key threat to the olive oil and winemaking sectors. The present study analyzes the impact of climate change on the potential yield of these two crops over the Côa region. For this purpose, two crop models were initialized and ran with state-of-the art spatial datasets for climate, soil, terrain, and plant data. As outputs of the crop models, potential yields of grapevines and olive trees were obtained for the recent-past (1981–2005) and for the future (2041–2070), under two climatic scenarios (RCP4.5 and RCP8.5). Results (potential yield) were then normalized, taking into account the recent-past maximum yields and divided into four classes (low, low-moderate, moderate-high, and high). For the recent-past, the results of the crop models present a high agreement with the current location of vineyards and olive groves. For the future, two different types of impacts (positive and negative) are found for the two crops. For olive trees, the results show promising future improvements in possible expansion areas within the Côa region. However, for grapevines, the results show a decrease in potential yields throughout the region, including a strong shift of producing moderate zones to low potentials. Nonetheless, these results also suggest that the negative impacts of climate change can be alleviated by the application of suitable adaptation measures, based on changing certain management practices, even in the more severe future scenario. Therefore, these measures should be carefully planned and implemented in a timely fashion by farmers.

Sustainable intensification of cereal cropping systems can contribute to reducing food insecurity while mitigating biodiversity losses thanks to land sparing. Yet, intensifying crop production with more nutrient inputs often increases sensitivity to climate hazards and yields inter-annual variability. How different options for sustainable intensification interact and how they could help achieve greater and less variable yields is not well known for key cereal cropping systems of sub-Saharan Africa. Here, we aim to assess the impact on productivity and its interannual variation of three contrasted strategies of crop intensification, namely i) the integration of cowpea intercropped with sorghum or as sole crop, ii) improved varieties with high harvest index and greater nitrogen use efficiency, and iii) the use of more mineral fertilizer. We did this assessment using a calibrated soil-crop model for a case study of sorghum-based cropping systems of semi-arid southern Mali, using a historical weather series from 1980 to 2010. We found that the incremental combinations of two or three strategies helped increase productivity in terms of energy and protein. However, greater productivity went hand in hand with greater inter-annual variability. Intercropping sorghum with cowpea helped to reduce the inter-annual variability of productivity, but only when mineral fertilizer was not added. With mineral fertilizer input, sorghum contributed the largest share of the intercrop productivity, and sorghum yield was more variable from a year to another than cowpea yield. The overall productivity of intercropping sorghum with cowpea was always greater than having the two sole crops on separate plots. This was possible thanks to the facilitation of light, water and N acquisition by the two crops in the intercropping, regardless of the specificities of the growing season with regard to water and nitrogen stress. However, the variability of productivity was smaller for the cultivation of the two sole crops on separate plots, regardless of fertilizer input. The often-claimed benefit of intercropping on reducing the inter-annual variability of production does not hold when the three strategies are combined. Our results suggest that the integration of legumes does not help cushion farmers against the risk related to the intensification of cropping systems with more nutrients and improved seeds. Careful gross margin analysis that accounts for price variability and uncertainty would be required to refine this risk assessment for specific farm contexts. We discuss the implication of these findings in light of the current research agenda for the sustainable intensification of cropping systems in sub-Saharan Africa. • We explored the impact of climate variability on legume diversification using a crop model. • The case study is land-constrained and semi-arid southern Mali. • Improved variety and mineral fertilizer contribute to increase variability of productivity. • Intercropping help decrease variability of productivity, but only without fertilizer input. • Integrating a sole legume stabilizes productivity, but with smaller gains than intercropping. [ABSTRACT FROM AUTHOR]

Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research. However, estimating many parameters simultaneously for different types of response variables is difficult. This becomes more complicated for grapevines with different phenotypes between varieties and training systems. Our study aims to evaluate how a standard least square approach can be used to calibrate a complex grapevine model for simulating both the phenology (flowering and harvest date) and yield of four different variety–training systems in the Douro Demarcated Region, northern Portugal. An objective function is defined to search for the best-fit parameters that result in the minimum value of the unweighted sum of the normalized Root Mean Squared Error (nRMSE) of the studied variables. Parameter uncertainties are estimated as how a given parameter value can determine the total prediction variability caused by variations in the other parameter combinations. The results indicate that the best-estimated parameters show a satisfactory predictive performance, with a mean bias of −2 to 4 days for phenology and −232 to 159 kg/ha for yield. The corresponding variance in the observed data was generally well reproduced, except for one occasion. These parameters are a good trade-off to achieve results close to the best possible fit of each response variable. No parameter combinations can achieve minimum errors simultaneously for phenology and yield, where the best fit to one variable can lead to a poor fit to another. The proposed parameter uncertainty analysis is particularly useful to select the best-fit parameter values when several choices with equal performance occur. A global sensitivity analysis is applied where the fruit-setting parameters are identified as key determinants for yield simulations. Overall, the approach (including uncertainty analysis) is relatively simple and straightforward without specific pre-conditions (e.g., model continuity), which can be easily applied for other models and crops. However, a challenge has been identified, which is associated with the appropriate assumption of the model errors, where a combination of various calibration approaches might be essential to have a more robust parameter estimation.

Objectives: This study was an attempt to replicate the findings from an earlier experimental evaluation of a probation officer training program by Bonta et al. (Criminal Justice and Behavior, 38: 1127–1148, 2011). An experimental design was used with an improvement in the random assignment of clients and was tested with a sample of probation officers from a new jurisdiction. Methods: Probation officers from the Canadian province of Alberta were randomly assigned to training or probation-as-usual. Officer behavior was measured by audio recordings of supervision sessions and recidivism was defined as a new conviction within 2 years of the initial recording. Attrition resulted in 27 probation officers submitting audio recordings of supervision sessions over a 6-month period (15 in the experimental group and 12 in the control). There were 160 recordings of 81 probationers submitted. Results: The audio recordings showed inconsistent changes in officer behavior and no differences in recidivism between the clients of the experimental and control probation officers. However, the use of cognitive techniques by the probation officers was associated with a longer time to recidivism. In addition, by 10 months, more than half of the trained officers stopped their involvement in ongoing professional development activities. Conclusion: Although the study failed to replicate the major findings reported by Bonta et al., it did highlight the importance of cognitive techniques in officer training. The results are interpreted with respect to the replication literature and the difficulties inherent in direct and conceptual replications especially in real-world settings. [ABSTRACT FROM AUTHOR]

Projected warming and drying trends over the Mediterranean region represent a substantial threat for wheat production. The present study assesses winter wheat yield response to potential climate change and estimates the quantitative effectiveness of using early flowering cultivars and early sowing dates as adaptation options for the major wheat production region of Portugal. A crop model (STICS) is used for this purpose, which is calibrated for yield simulations before projecting future yields. Climate projections over 2021–2050 and 2051–2080 under two emission scenarios (RCP4.5 and RCP8.5) are retrieved from bias-adjusted datasets, generated by a ten-member climate model ensemble. Projected intensification of water deficits and more frequent high-temperature events during late spring (April–June), coinciding with the sensitive grain filling stage, primarily result in continuous mean yield losses (relative to 1981–2010) by − 14% (both scenarios) during 2021–2050 and by − 17% (RCP4.5) or − 27% (RCP8.5) during 2051–2080, also accompanied by increased yield variabilities. Of evaluated adaptation options at various levels, using earlier flowering cultivars reveals higher yield gains (26–38%) than that of early sowings (6–10%), which are able to reverse the yield reductions. The adopted early flowering cultivars successfully advance the anthesis onset and grain filling period, which reduces or avoids the risks of exposure to enhanced drought and heat stresses in late spring. In contrast, winter warming during early sowing window could affect vernalization fulfillment by slowing effective chilling accumulation, thus increasing the pre-anthesis growth length with limited effects on advancing reproductive stage. Crop yield projections and explored adaptation options are essential to assess food security prospects (availability and stability) of dry Mediterranean areas, providing crucial insights for appropriate policymaking. [ABSTRACT FROM AUTHOR]

Abstract In this study, we analyzed the patterns of relationships between multiple ecosystem services in apple orchards by considering the cascade that links agricultural practices to ecosystem functions and then to ecosystem services. Five major ecosystem services were considered: fruit production, soil nitrogen availability, climate regulation, water cycle maintenance and regulation, including water quality, and pest and disease control. We derived indicators of ecosystem functions and of ecosystem services from model simulations of orchards driven by virtual cropping systems combining various modalities of nitrogen fertilization, irrigation, and pest control. We deciphered the links between practices and ecosystem functions and between those functions and ecosystem services and clustered cropping systems according to their ecosystem service supply. Noticeable synergies were found between yield, fruit mass and sequestrated carbon. The contribution of carbon allocation to fruit in sequestrated carbon was considerable. Nitrogen absorption, impacted by fertilization and irrigation, was a major driver of these relationships. The typology built from these virtual cropping systems clearly followed a gradient of provisioning and regulating ecosystem services. Five cropping systems optimized the compromise between provisioning and regulating services and were essentially characterized by organo-mineral fertilization, comfort irrigation, apple scab-resistant cultivars and exclusion nets against codling moth. Our approach could contribute to the design of cropping systems that would provide an acceptable compromise between multiple ecosystem services in orchards. Highlights • Virtual cropping systems created in silico were simulated using two models. • Noticeable synergies and tradeoffs between ecosystem services were found. • The system clustering presented a gradient of marketed and non-marketed ecosystem services. • Five cropping systems optimized the ecosystem services compromise. [ABSTRACT FROM AUTHOR]

Highlights • We compared the models BASGRA, CATIMO and STICS for their predictions of nutritive value in timothy. • Model parameters were calibrated for each cultivar separately and for all cultivars together against field trials in Canada and Northern Europe. • The prediction accuracy of neutral detergent fibre attributes was in the same range as previous predictions of forage dry matter yield. • The predictions of crude protein were less accurate than the predictions of neutral detergent fibre attributes. Abstract Different forage grass models are used to simulate forage yield and nutritive attributes, but these models are seldom compared, particularly those for timothy (Phleum pratense L.), a widely grown forage grass species in agricultural regions with a cold temperate climate. We compared the models BASGRA, CATIMO and STICS for their predictions of timothy crude protein (CP) concentration, neutral detergent fibre (NDF) concentration and NDF digestibility (dNDF), three important forage nutritive attributes. Data on CP and NDF concentrations, and dNDF and the associated weather and soil data for seven cultivars, taken from eight field experiments in Canada, Finland, Norway, and Sweden, were divided into calibration and validation datasets. Model parameters were estimated for each cultivar separately (cultivar-specific calibration) and for all cultivars together (generic calibration), using different methods for the three models. Normalized root mean square error (RMSE) in prediction of CP concentration varied between 16 and 26% for BASGRA, 45 and 101% for CATIMO and 23 and 40% for STICS across the two calibration methods and the calibration and validation datasets. Normalised RMSE in prediction of NDF concentration varied between 8 and 13% for BASGRA, 14 and 21% for CATIMO and 8 and 12% for STICS, while for dNDF it varied between 7 and 22% for BASGRA, 7 and 38% for CATIMO and 5 and 6% for STICS. Cultivar-specific calibration improved the performance of CATIMO and STICS, but not BASGRA, compared with generic calibration. The prediction accuracy for NDF concentration and dNDF with the three models was within the same range or better than that for forage dry matter (DM) yield of timothy. Overall, the three models performed well in predicting some nutritive attributes and yield in Northern Europe and Canada, but improvements are required, particularly to increase the prediction accuracy of CP concentration. [ABSTRACT FROM AUTHOR]

Cultivar recommendation is a key factor in cropping system management. Classical approaches based on comparative multi-environmental trials can hardly explore the agro-climatic and management heterogeneity farmers may have to face. Moreover, they struggle to keep up with the number of genotypes commercially released each year. We propose a new approach based on the integration of in silico ideotyping and functional trait profiling, with the common bean (Phaseoulus vulgaris L.) in Northern Italy as a case study. Statistical distributions for six functional traits (light extinction coefficient, radiation use efficiency, thermal time to first pod and maturity, seed weight, plant height) were derived for 24 bean varieties. The analysis of soil, climate and management in the study area led us to define 21 homogeneous contexts, for which ideotypes were identified using the crop model STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard), the E-FAST (Extended Fourier Amplitude Sensitivity Test) sensitivity analysis method, and the distributions of functional traits. For each context, the 24 cultivars were ranked according to the similarity (weighted Euclidean distance) with the ideotype. Context-specific ideotypes mainly differed for phenological adaptation to specific combinations of climate and management (sowing time) factors, and this reflected in the cultivar recommendation for the different contexts. Feedbacks from bean technicians in the study area confirmed the reliability of the results and, in turn, of the proposed methodology.

Although crop diversification is one of the main strategy of agroecological transition, a major obstacle is the lack of local references regarding new crops. Land suitability methods can provide a rapid screening of crop suitability in a region. However, mainstream methods are mainly based on assessing soil and climate suitability, whereas it is fundamental to know where and how a new crop can be introduced into existing crop rotations and whether this introduction would be profitable. Our method based on recent advances in the characterization of cropping systems at the regional level can be exploited to evaluate: (1) the yield potential of the new crop, (2) the potential of this new crop being successfully introduced into ongoing crop rotations; (3) the economic benefits of such an introduction. The method was tested for the possible introduction of new soybean varieties in northern France. The method developed has relevance beyond the case study. Our method could also be easily adapted to rapidly assess the potential for introducing new crops in cases where there climate database, soil map information and a Land Parcel Identification System are available.

The EU directive has addressed ambitious targets concerning the quality of water bodies. Predicting water quality as affected by land use and management requires using dynamic agro-hydrogeological models. In this study, an agronomic model (STICS) and a hydrogeological model (MODCOU) have been associated in order to simulate nitrogen fluxes in the Seine-Normandie Basin, which is affected by nitrate pollution of groundwater due to intensive farming systems. This modeling platform was used to predict and understand the spatial and temporal evolution of water quality over the 1971-2013 period. A quality assurance protocol (Refsgaard et al. Environ Model Softw 20: 1201-1215, 2005) was used to qualify the reliability of STICS outputs. Four iterative runs of the model were carried out with improved parameterization of soils and crop management without any change in the model. Improving model inputs changed much more the spatial distribution of simulated N losses than their mean values. STICS slightly underestimated the crop yields compared to the observed values at the administrative district scale. The platform also slightly underestimated the nitrate concentration at the outlet level with a mean difference ranging from −1.4 to −9.2 mg NO3 L−1 according to the aquifer during the last decade. This outcome should help the stakeholders in decision-making to prevent nitrate pollution and provide new specifications for STICS development. [ABSTRACT FROM AUTHOR]

Climate change projections for Portugal showed warming and drying trends, representing a substantial threat for the sustainability of forage production in perennial grassland. The objective of the present study was to assess climate change impacts on seasonal dry matter yield (DMY) in three locations (North-west-, Central-inner and South-Portugal) with different climatic conditions, for two grassland production systems deviating in growing season length, either early cuts in spring (ES) or late cuts in summer (LS). Impacts were estimated using the STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard) crop model, by comparing a historical baseline period (1985'2006) with simulated projections over future periods (2021'2080). For this purpose, the STICS crop model was driven by high-resolution climate data from a coupled Global Climate Model/Regional Climate Model chain. As a result, we obtained that, during the baseline period, DMYof LS was consistently much higher than that of ES in all three locations. For LS, significant reductions in meanDMY were forecasted during 2061'2080, ranging from mild ('13%) in the north to severe ('31%) in the south of Portugal. In contrast, seasonal DMY was largely maintained for ES among sites until 2080, benefiting from low water deficits, the expected atmospheric CO2 rise and the forecasted temperature increase during cool season. Thus, the yield gap was projected to gradually decrease between the two regimes, in which mean DMY for ES was foreseen to exceed that of LS over 2061'2080 in the southern site. Moreover, ES was projected to have very low exposure to extreme heat and severe water stresses. Conversely, LS, subjected to high summer water deficit and irrigation needs, was projected to experience increased summertime water stress (9'11%) and drastically increased heat stress (33'57%) in 2061'2080, with more pronounced heat stress occurring in the south. Frequency of occurrence of extreme heat stress was projected to gradually increase in summer over successive study periods, with a concomitant increased intensity of DMY response to interannual variability of heat stress during 2061'2080. Heat stress tended to be more important than water stress under the prescribed irrigation strategy for LS, potentially being the main limiting factor for summertime DMY production under climate change scenario. [ABSTRACT FROM AUTHOR]

Fruit tree production faces the major challenge of ensuring maximal productivity with due consideration for the environment and human health. The increasingly recognized concept of ecosystem service could help to address this duality. In this paper, we propose an analytical framework based on a soil crop model to investigate how agricultural management and pedoclimatic conditions affect the joint production of marketed and non-marketed ecosystem services through underlying ecosystem functions in apple orchards. The ecosystem services considered on an annual scale were soil nitrogen availability, climate regulation, water regulation and fruit production. Ecosystem functions and services were described by specific indicators that were quantified using the STICS soil crop model. This model was parameterized using data collected on two experimental apple orchard sites under conventional and low-input or organic management in southeastern France. The interdependencies between environmental components, cultural operations and ecosystem functions were dynamically integrated by the model and highlighted significant interactions between the indicators of ecosystem services. Thus, the service indicators soil organic nitrogen variation and the prevention of nitrogen denitrification and of leaching were positively correlated and in conflict with soil mean nitrate concentration and mean soil humidity. They were also linked negatively to nitrogen mineralization enhanced by irrigation and positively to soil carbon sequestration impacted by fertilization; these two functions were impacted by soil conditions. Yield and carbon sequestration presented a strong synergy and were positively correlated to nitrogen absorption increased by mineral fertilization. Globally, nitrogen fertilization management and planting density were particularly important for the delivery of multiple ecosystem services, but soil and climate effects were far from negligible, especially for nitrogen and water-related services. The ecosystem service profiles of the studied cropping systems were diversified, with contrasted profiles showing high yield and carbon sequestration but low prevention of nitrogen denitrification and of nitrogen leaching, and more balanced profiles. The STICS crop model made it possible to quantify and analyze profiles of ecosystem services and should be helpful in instrumenting the dialogue between fruit growers and other stakeholders by simulating scenarios to optimize multiple services. However, it has to be improved to address the impact of grass cover on soil functions and the long-term functioning of apple orchards. [ABSTRACT FROM AUTHOR]

Process-based models are useful tools for estimating the complex interactions between plant, soil and climate systems, assessments which are necessary for improving nutrient cycling and reducing trace gas emissions. Incorporation of knowledge gained through new research is ongoing, thus there is a need for evaluation of model processes and process interactions. In this study, three sites in eastern Canada (St. Bruno, St. Jean, and Ottawa) planted with spring wheat during the years 1993-2007 were used to evaluate and compare the water and N process simulations of the models DayCent, DNDC, and STICS. The simulated soil moisture by all models was generally well represented with low ARE (< 8%) and an EF > 0.1. The unsaturated flow mechanism included in DayCent further improved soil moisture estimates compared to the other models. When sufficient replicate data was available measurement variability was considered, resulting in soil nitrogen being only slightly underestimated (ARE of −10, −1, and −22%, for DayCent, DNDC, and STICS, respectively). On average across the three sites, considering all statistics, the DNDC model proved to be most accurate for simulating mineral N, followed by DayCent and then STICS. Continued process model development is reliant on measurement datasets that can accurately represent carbon and nitrogen dynamics. Frequently, site specific biases convolute model mechanism evaluation and thus assessments have to be conducted across numerous sites to better benchmark model performance. On this premise a comprehensive multi-site inter-model mechanism evaluation was conducted and future model development needs were identified. [ABSTRACT FROM AUTHOR]

The future of global rice productions in top producing countries is undermined by the impact of climate change threatening food security in the near future. In those European Mediterranean areas where rice is cultivated, this peculiar cropping system plays a crucial role in terms of sociocultural and ecological issues, and the climate change impact is still scarcely investigated. In this study, we explored the future trends of potential rice yields in the region considering the multiple sources of uncertainty associated with climate and yield predictions. Two rice crop models (STICS and WARM) were calibrated using 20 field experiments carried out in two main European rice producing areas − i.e., the Italian Lomellina and the French Camargue. These models were then applied under a range of climate change scenarios in 2030 and 2070 time frames, considering projections from the combination of four General Circulation Models and two extreme Representative CO 2 Concentration Pathways (RCP 2.6 and 8.5). We compared the simulated yield levels with no adaptation, and designed adaptation strategies based on the anticipation of sowing date and the adoption of varieties with longer crop cycle. Our results showed that with no adaptation yields would decrease on average by 8% in 2030 and 12% in 2070 in Camargue and Lomellina. Future simulated yields in the two areas were lower than in the baseline in 67% (Camargue) and 84% (Lomellina) of the cases. The implementation of both adaptation strategies proved to be effective in reversing the situation, leading to an average yield increase of 28% and 25% in 2030 and 2070, respectively. The associated probability of lower yields than in current conditions was 24% in the two sites. Despite the uncertainty in predictions, mainly related to site, GCM and RCP, our findings indicate that the European rice sector has the potential to enhance current production levels in a changing climate, if longer cycle varieties will be grown in Mediterranean rice areas. [ABSTRACT FROM AUTHOR]

Maize is an important crop for the Portuguese agricultural sector. Future climate change, with warmer and dryer conditions in this Mediterranean environment, will challenge this high-water demanding crop. The present study aims at assessing the response of maize yield, growth cycle, seasonal water input and daily water productivity (DWP) to climate change, and analyse water-yield relations. For this purpose, two process-based crop models are used (STICS and AquaCrop) and were validated in simulating irrigated maize yields in Central Portugal (Ribatejo) by using regional statistics (1986–2005). Both models show an overall agreement in their outputs. The 2-model mean outputs are considered under future climate projections (2021–2080; RCP4.5 and 8.5), using the global/regional climate model chain M-MPI-ESM-LR/SMHI-RCA4. The most significant reductions on maize yield (−17%), growth cycle (−12%) and DWP (−19%) are observed for 2061–2080 under RCP8.5, with a noticeable decrease of seasonal water input (−9%) during 2041–2060. Decreased DWP is largely due to significant yield reduction, with limited benefit of atmospheric CO 2 enrichment. A water-yield relation analysis highlights that an increase of 2–14% in irrigation for future scenarios (compared to 1986–2005) might be a suitable strategy to mitigate yield reduction, despite substantially lower DWP (down to −23%). These findings demonstrate that our model approach can be used as a decision support tool by Portuguese farmers, particularly in optimizing maize production under changing climates. [ABSTRACT FROM AUTHOR]

The current challenge in sustainable agriculture is to introduce new cropping systems to reduce pesticides use in order to reduce ground and surface water contamination. However, it is difficult to carry out in situ experiments to assess the environmental impacts of pesticide use for all possible combinations of climate, crop, and soils; therefore, in silico tools are necessary. The objective of this work was to assess pesticides leaching in cropping systems coupling the performances of a crop model (STICS) and of a pesticide fate model (MACRO). STICS-MACRO has the advantage of being able to simulate pesticides fate in complex cropping systems and to consider some agricultural practices such as fertilization, mulch, or crop residues management, which cannot be accounted for with MACRO. The performance of STICS-MACRO was tested, without calibration, from measurements done in two French experimental sites with contrasted soil and climate properties. The prediction of water percolation and pesticides concentrations with STICS-MACRO was satisfactory, but it varied with the pedoclimatic context. The performance of STICS-MACRO was shown to be similar or better than that of MACRO. The improvement of the simulation of crop growth allowed better estimate of crop transpiration therefore of water balance. It also allowed better estimate of pesticide interception by the crop which was found to be crucial for the prediction of pesticides concentrations in water. STICS-MACRO is a new promising tool to improve the assessment of the environmental risks of pesticides used in cropping systems. [ABSTRACT FROM AUTHOR]

Characterization of the soil water reservoir is critical for understanding the interactions between crops and their environment and the impacts of land use and environmental changes on the hydrology of agricultural catchments especially in tropical context. Recent studies have shown that inversion of crop models is a powerful tool for retrieving information on root zone properties. Increasing availability of remotely sensed soil and vegetation observations makes it well suited for large scale applications. The potential of this methodology has however never been properly evaluated on extensive experimental datasets and previous studies suggested that the quality of estimation of soil hydraulic properties may vary depending on agro-environmental situations. The objective of this study was to evaluate this approach on an extensive field experiment. The dataset covered four crops (sunflower, sorghum, turmeric, maize) grown on different soils and several years in South India. The components of AWC (available water capacity) namely soil water content at field capacity and wilting point, and soil depth of two-layered soils were estimated by inversion of the crop model STICS with the GLUE (generalized likelihood uncertainty estimation) approach using observations of surface soil moisture (SSM; typically from 0 to 10 cm deep) and leaf area index (LAI), which are attainable from radar remote sensing in tropical regions with frequent cloudy conditions. The results showed that the quality of parameter estimation largely depends on the hydric regime and its interaction with crop type. A mean relative absolute error of 5% for field capacity of surface layer, 10% for field capacity of root zone, 15% for wilting point of surface layer and root zone, and 20% for soil depth can be obtained in favorable conditions. A few observations of SSM (during wet and dry soil moisture periods) and LAI (within water stress periods) were sufficient to significantly improve the estimation of AWC components. These results show the potential of crop model inversion for estimating the AWC components of two-layered soils and may guide the sampling of representative years and fields to use this technique for mapping soil properties that are relevant for distributed hydrological modelling. [ABSTRACT FROM AUTHOR]

Viticulture is a key socio-economic sector in Europe. Owing to the strong sensitivity of grapevines to atmospheric factors, climate change may represent an important challenge for this sector. This study analyses viticultural suitability, yield, phenology, and water and nitrogen stress indices in Europe, for present climates (1980-2005) and future (2041-2070) climate change scenarios (RCP4.5 and 8.5). The STICS crop model is coupled with climate, soil and terrain databases, also taking into account CO2 physiological effects, and simulations are validated against observational data sets. A clear agreement between simulated and observed phenology, leaf area index, yield and water and nitrogen stress indices, including the spatial differences throughout Europe, is shown. The projected changes highlight an extension of the climatic suitability for grapevines up to 55°N, which may represent the emergence of new winemaking regions. Despite strong regional heterogeneity, mean phenological timings (budburst, flowering, veraison and harvest) are projected to undergo significant advancements (e.g. budburst/harvest can be >1 month earlier), with implications also in the corresponding phenophase intervals. Enhanced dryness throughout Europe is also projected, with severe water stress over several regions in southern regions (e.g. southern Iberia and Italy), locally reducing yield and leaf area. Increased atmospheric CO2 partially offsets dryness effects, promoting yield and leaf area index increases in central/northern Europe. Future biomass changes may lead to modifications in nitrogen demands, with higher stress in northern/central Europe and weaker stress in southern Europe. These findings are critical decision support systems for stakeholders from the European winemaking sector. [ABSTRACT FROM AUTHOR]

Future progress in wheat yield will rely on identifying genotypes and management practices better adapted to the fluctuating environment. Nitrogen (N) fertilization is probably the most important practice impacting crop growth. However, the adverse environmental impacts of inappropriate N management (e.g., lixiviation) must be considered in the decision-making process. A formal decisional algorithm was developed to tactically optimize the economic and environmental N fertilization in wheat. Climatic uncertainty analysis was performed using stochastic weather time-series (LARS-WG). Crop growth was simulated using STICS model. Experiments were conducted to support the algorithm recommendations: winter wheat was sown between 2008 and 2014 in a classic loamy soil of the Hesbaye Region, Belgium (temperate climate). Results indicated that, most of the time, the third N fertilization applied at flag-leaf stage by farmers could be reduced. Environmental decision criterion is most of the time the limiting factor in comparison to the revenues expected by farmers. [ABSTRACT FROM AUTHOR]

Development and use of models to predict and study the production and the environmental impacts of biomass cropping systems are of great interest for their sustainable development. Improvements were made to the research version of the STICS crop-soil model in order to simulate biomass production and environmental impacts of Miscanthus × giganteus cropping systems in the long term. This research version was then validated on a large database and in various pedoclimatic environments in France and UK. The model accurately simulated biomass production and nitrogen (N) content in aboveground biomass, from planting until 4 to 20 years of cultivation. The model efficiency (EF) was 0.80 and 0.64 for biomass and N content, respectively, and the values of relative RMSE were 23 and 31 %. Soil water and mineral N contents were also satisfactorily predicted (EF = 0.96 and 0.42; relative RMSE = 10 and 72 %). The model accurately reproduced the effect of management practices on the harvested biomass and N export. Yield gap analysis using simulations with and without active stresses revealed that Miscanthus × giganteus biomass production was limited by both water and N availability during the establishment phase but mainly limited by water availability during the post-establishment phase. The STICS crop-soil model can accurately predict Miscanthus × giganteus biomass production and environmental impacts such as water drainage and nitrate leaching and compare strategies with varying N fertilization, irrigation and harvest date. [ABSTRACT FROM AUTHOR]

The study of cell-nanoparticle interactions is an important aspect for understanding drug delivery using nanocarriers. In this regard, advances in fluorescence based microscopy are useful for the investigation of temporal and spatial behavior of nanoparticles (NPs) within the intracellular environment. In this work, we focus on the delivery of the naturally-occurring hydrophobic photosensitizer Hypericin in human lung carcinoma A549 cells by using biodegradable poly l -lactic acid NPs. For the first time, Hypericin containing NPs are prepared by combining the miniemulsion technique with the solvent evaporation method. This approach yields an efficient loading of the NPs with Hypericin and allows for additional cargo molecules. To monitor the release of Hypercin from the NPs, an additional fluorescent lipophilic dye Coumarin-6 is incorporated in the NPs. Temporal and spatiotemporal image correlation spectroscopy is used to determine the fate of the NPs carrying the potential cargo. Both directed and non-directed motions are detected. By using image cross-correlation spectroscopy and specific fluorescent labeling of endosomes, lysosomes and mitochondria, the dynamics of the cargo loaded NPs in association with the organelles is studied. [ABSTRACT FROM AUTHOR]

Within the context of nitrogen (N) management, since 1950, with the rapid intensification of agriculture, farmers have often applied much larger fertiliser quantities than what was required to reach the yield potential. However, to prevent pollution of surface and groundwater induced by nitrates, The European Community launched The European Nitrates Directive 91/6/76/EEC. In 2002, in Wallonia (Belgium), the Nitrates Directive has been transposed under the Sustainable Nitrogen Management in Agriculture Program (PGDA), with the aim of maintaining productivity and revenue for the country’s farmers, while reducing the environmental impact of excessive N application. A feasible approach for addressing climatic uncertainty lies in the use of crop models such as the one commonly known as STICS (simulateur multidisciplinaire pour les cultures standard). These models allow the impact on crops of the interaction between cropping systems and climatic records to be assessed. Comprehensive historical climatic records are rare, however, and therefore the yield distribution values obtained using such an approach can be discontinuous. In order to obtain better and more detailed yield distribution information, the use of a high number of stochastically generated climate time series was proposed, relying on the LARS-Weather Generator. The study focused on the interactions between varying N practices and climatic conditions. Historically and currently, Belgian farmers apply 180 kg N ha −1 , split into three equal fractions applied at the tillering, stem elongation and flag-leaf stages. This study analysed the effectiveness of this treatment in detail, comparing it to similar practices where only the N rates applied at the flag-leaf stage were modified. Three types of farmer decision-making were analysed. The first related to the choice of N strategy for maximising yield, the second to obtaining the highest net revenue, and the third to reduce the environmental impact of potential N leaching, which carries the likelihood of taxation if inappropriate N rates are applied. The results showed reduced discontinuity in the yield distribution values thus obtained. In general, the modulation of N levels to accord with current farmer practices showed considerable asymmetry. In other words, these practices maximised the probability of achieving yields that were at least superior to the mean of the distribution values, thus reducing risk for the farmers. The practice based on applying the highest amounts (60–60–100 kg N ha −1 ) produced the best yield distribution results. When simple economical criteria were computed, the 60–60–80 kg N ha −1 protocol was found to be optimal for 80–90% of the time. There were no statistical differences, however, between this practice and Belgian farmers’ current practice. When the taxation linked to a high level of potentially leachable N remaining in the soil after harvest was considered, this methodology clearly showed that, in 3 years out of 4, 30 kg N ha −1 could systematically be saved in comparison with the usual practice. [ABSTRACT FROM AUTHOR]

Soil–crop models are increasingly used as predictive tools to assess yield and environmental impacts of agriculture in a growing diversity of contexts. They are however seldom evaluated at a given time over a wide domain of use. We tested here the performances of the STICS model (v8.2.2) with its standard set of parameters over a dataset covering 15 crops and a wide range of agropedoclimatic conditions in France. Model results showed a good overall accuracy, with little bias. Relative RMSE was larger for soil nitrate (49%) than for plant biomass (35%) and nitrogen (33%) and smallest for soil water (10%). Trends induced by contrasted environmental conditions and management practices were well reproduced. Finally, limited dependency of model errors on crops or environments indicated a satisfactory robustness. Such performances make STICS a valuable tool for studying the effects of changes in agro-ecosystems over the domain explored. [ABSTRACT FROM AUTHOR]

Biomass production by perennial plants promises to increase land use efficiency and reduce greenhouse gas emissions from cropping systems dedicated to bioenergy production. The modelling of both biomass production and the environmental impacts of these systems over the long term is needed in order to evaluate their sustainability. New equations have been added to the STICS soil-crop-atmosphere model to provide a better description of perennial organs and their relationship with non-perennial ones, corresponding to the rhizomes and shoots, respectively in the Miscanthus × giganteus case study. Their description is intended to be generic for perennial plants, supported by the functional approach of STICS. The new version of STICS 8 was calibrated using published data and then validated against independent data. It was able to simulate the biomass and nitrogen content of the shoots (with a model efficiency of 0.95 and 0.70, respectively) and reproduce the dynamic of biomass and nitrogen in perennial organs (with a model efficiency of 0.41 and 0.63, respectively). Some of the model's improvements are discussed. Modifications to the model allowed simulations of the effect of cultural practices, such as nitrogen fertilisation or harvest date, on the biomass and nitrogen content of rhizomes and shoots. [ABSTRACT FROM AUTHOR]

Highlights: [•] We model percolation and nitrate leaching for three different crops. [•] All the experiments took place under Western Mediterranean climate. [•] Although the agricultural practices from farmers are sound, high percolation a nitrate leaching values were obtained. [•] This result is a consequence of climate regimen. [ABSTRACT FROM AUTHOR]

Abstract: This study presents a Bayesian approach for the parameters' identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L.) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modelling. [Copyright &y& Elsevier]

The Fallopian tube epithelium harbors the origin cells for the majority of high-grade serous ovarian carcinomas (HGSCs), the most lethal form of gynecologic malignancies. PAX8 belongs to the paired-box gene family of transcription factors and it is a marker of the FTE secretory cell lineage. Its role has been investigated in migration, invasion, proliferation, cell survival, stem cell maintenance, angiogenesis and tumor growth. In this review, we focus on the pro-tumorigenic role of PAX8 in ovarian cancer; in this context, PAX8 possibly continues to exert its transcriptional activity on its physiological targets but may also function on newly available targets after the tumorigenic hits. Acquiring new insights into the different PAX8 mechanism(s) of action in the tumor microenvironment could uncover new viable therapeutic targets and thus improve the current treatment regimen., Competing Interests: The authors report no conflicts of interest in this work., (© 2022 Di Palma and Zannini.)

In Europe, most of vineyards are managed under rainfed conditions, where water deficit has become increasingly an issue. The flowering-veraison phenophase represents an important period for vine response to water stress, which is known to depend on variety characteristics, soil and climate conditions. In this paper, we have carried out a retrospective analysis for important European wine regions over 1986–2015, with objectives to assess the mean Crop Water Stress Indicator (CWSI) during flowering-veraison phase, and potential Yield Lose Rate (YLR) due to seasonal cumulative water stress. Moreover, we also investigate if advanced flowering-veraison phase can lead to alleviated CWSI under recent-past conditions, thus contributing to reduced YLR. A process-based grapevine model is employed, which has been extensively calibrated for simulating both flowering and veraison stages using location-specific observations representing 10 different varieties. Subsequently, grid-based modelling is implemented with gridded climate and soil datasets and calibrated phenology parameters. The findings suggest wine regions with higher mean CWSI of flowering-veraison phase tend to have higher potential YLR. However, contrasting patterns are found between wine regions in France-Germany-Luxembourg and Italy-Portugal-Spain. The former tends to have slight-to-moderate drought conditions (CWSI<0.5) along with a negligible-to-moderate YLR (<30%), whereas the latter is found to have severe-to-extreme drought (CWSI>0.5) and substantial YLR (>40%). Wine regions prone to a high drought risk (CWSI>0.75) are also identified, which are concentrated in southern Mediterranean Europe. Advanced flowering-veraison phase over 1986–2015, could have benefited from more spring precipitation and cooler temperatures for wine regions of Italy-Portugal-Spain, leading to reduced mean CWSI and YLR. For those of France-Germany-Luxembourg, this can have reduced flowering-veraison precipitation, but prevalent reductions of YLR are also found, possibly due to shifted phase towards a cooler growing-season with reduced evaporative demands. Our study demonstrates flowering-verasion water deficit is critical for potential yield, which can have different impacts between Central and Southern European wine regions. This phase can be advanced under a warmer climate, thus having important implications for European rainfed vineyards. The overall outcome may provide new insights for appropriate viticultural management of seasonal water deficits under climate change. • Water stress of a drought-sensitive period is assessed for European rainfed vineyards. • Wine regions prone to a high flowering-veraison drought risk are identified. • Flowering-veraison water stress strongly affects water-limited potential yields. • Advanced flowering-veraison phase differently impacts European wine regions. • The outcomes improve understandings of seasonal water deficits of rainfed viticulture. [ABSTRACT FROM AUTHOR]

Highlights: [•] First calibration of STICS model for timothy. [•] Equations used in CATIMO model for calculating nutritive value indices successfully used and calibrated in STICS. [•] Good overall model performance (LAI, biomass and nutritive value) for various sites and climatic years. [•] Better model performance for simulating the primary growth than the regrowth. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this study is to evaluate the performances of estimating agronomic variables, such as total above ground biomass at key stages, or yield, from LAI data that could potentially be obtained from remote sensing observations. Approaches based either on empirical relationships or on forcing LAI within the STICS model () are considered, with emphasis on the effect of the accuracy and frequency of LAI data used. Both actual and simulated case studies on wheat for Northern France conditions were investigated under several levels of knowledge of the model input parameters and initial conditions. The results highlight the interest of using model based approaches for the estimation of agronomic variables. Forcing LAI data into the crop model allows compensating for the lack of detailed knowledge on management practices or soil characteristics. However, error and frequency of LAI observations may have an important impact on the estimation of agronomic variables, particularly for the early growth stages. In these conditions, an empirical approach, based on the calibration of a relationship between LAI at a given stage and the agronomic variable, provides an efficient alternative, though the validity of empirical relationships depends greatly on the database on which they have been obtained. [Copyright &y& Elsevier]

Traditionally, the role of a community supervision officer has largely been that of a case manager. However, knowledge in the area of 'what works' in offender rehabilitation has stimulated efforts to revolutionise what it means to supervise clients in the community; that is, moving from a case-management approach to what we call a 'change agent' approach. In this article, we define what cognitive-behaviourism looks like in a criminal justice context and how it can be used to maximize the impact of community supervision. Through the amalgamation of cognitive-behavioural techniques and risk/need information, we propose the use of a theoretically and empirically based framework (i.e. the STICS Action Plan) to assist Community Supervision Officers in planning, prioritising and effectively achieving change with their clients. [ABSTRACT FROM AUTHOR]

Crop growth models are great tools for studying and anticipating the future impacts of rising demands for agricultural production while satisfying constraints with respect to product safety, the landscape, and the environment. Before crop growth models can be applied, however, they need to be calibrated and evaluated for cultivars representative of a given ecozone. This study presents an evaluation of the STICS crop growth model using maize cultivar parameters calibrated for the Mixedwood Plains ecozone in Eastern Canada. In the study area, which extends from southwestern Quebec to southern Ontario, the available crop heat units (CHU, in CHU index) for plant growth vary between 2,500 and 3,500 CHU. One cultivar was first calibrated in the STICS model using leaf area index (LAI) and yield data from Ottawa, Ontario. The model gave good predictions of LAI, biomass, and yield for the cultivar CanMaïsNE in the range of 2,500-2,900 CHU. The root mean square error of the predictions was 28.1% for LAI, 17.5% for biomass, and 10.1% for yield. A second cultivar, CanMaïsSE, was defined for the higher CHU range (2,900-3,300 CHU). CanMaïsSE had the same crop and cultivar parameters as CanMaïsNE except for the duration of grain filling, which was increased by 6-7 days to account for the longer growing season in the area with 3,300 CHU. Good predictions of LAI, biomass, and yield were obtained for CanMaïsSE, with root mean square error values of 30.6%, 25.2%, and 16.1%, respectively. Defining these two generic maize cultivars was sufficient to estimate biomass, yield, and LAI over the entire study area. This work is the first calibration and performance evaluation of the STICS crop model for maize in North America. Moreover, these new grain maize cultivars, adapted to a shorter growing season, open new opportunities for using STICS in northern countries. [ABSTRACT FROM AUTHOR]

Abstract: Parameter estimation for complex process models used in agronomy or the environmental sciences is important, because it is a major determinant of model predictive power, and difficult, because the models and associated data are complex. Statistics provides guidance for parameter estimation under various assumptions concerning model error, but it is hard to know which assumptions are most acceptable for these models. We therefore propose a collection of parameter estimation methods. All are based on weighted least squares, but different assumptions lead to different weights. The methods allow one to fit simultaneously several different response variables. One can assume that all errors are independent or on the contrary are correlated. One can assume that model error has expectation zero or not. A software package called OptimiSTICS has been developed, that allows one to implement all of the proposed methods with the STICS crop-soil model. The software can in addition treat the case where some parameters are genotype specific while others are common to all genotypes. The software can also automatically do several sequential stages of parameter estimation. An example is presented, which shows the information that can be obtained, and the conclusions drawn, from comparing the different estimation methods. [Copyright &y& Elsevier]

Abstract: Crop modelling at a regional scale is often limited by the availability of input data. Soil databases can be an efficient way to provide spatial information regarding soil properties, and remote sensing has proved to be a valuable source of information of management practices through assimilation of variable, like leaf area index (LAI), in crop model. However, the large numbers of cultivars from seed companies used at the regional scale remain an important concern for regional crop modelling. The aim of this study is to test whether we can limit calibration to only one cultivar per crop for modelling LAI, biomass and yield dynamics in the Mixedwood Plains ecozone, which extends over 3° of latitude and 10° of longitude in Eastern Canada. The ability to use the new regionally adapted soybean cultivar (CanSoyEst) and spring wheat cultivar (CanBleEst) to predict LAI, biomass, and yield in the studied area was evaluated using data from several sites distributed between Southwestern Quebec and Southern Ontario. The model used is STICS, a generic open code crop model, which offers easy access to cultivar parameters and the ability to assimilate LAI from remote sensing to derive input data for regional scale studies. The CanBleEst cultivar provided good estimates of biomass and yield, with a root mean square error (RMSE) ranging between 10% and 20%. More scatter was observed for LAI estimates with a RMSE ranging from 21% to 25% depending on the sites. For the CanSoyEst cultivar, the predicted biomass had a RMSE ranging from 23% to 27% and the RMSE was about 26% for yield predictions whereas the predicted LAI showed more scatter (28%≤RMSE≤38%). A higher exposure to water stress and leaf shedding during senescence may explain why soybean output variables were predicted with more scatter. This work demonstrates that one cultivar per crop (i.e., soybean and spring wheat) provides enough accuracy to predict LAI, biomass and yield over the entire region. It provides a new soybean cultivar parameterization adapted to a short growing season and the first calibration and evaluation of a spring wheat cultivar in the STICS crop model. These contributions open new opportunities for using STICS in Northern climates characterized by short growing seasons. It also offers a foundation for crop modelling and prediction at regional scale in Eastern Canada. [Copyright &y& Elsevier]

In the World Heritage Côa region, in northern Portugal, agriculture has crucial economic, social and cultural importance. Vineyards and olive groves are part of the economic base of this region, contributing to the regional commercial budget and the livelihood of its residents. Climate change is expected to have significant impacts on these crops, where climatic conditions are already very warm and dry, posing a key threat to the olive oil and winemaking sectors. The present study analyzes the impact of climate change on the potential yield of these two crops over the Côa region. For this purpose, two crop models were initialized and ran with state-of-the art spatial datasets for climate, soil, terrain, and plant data. As outputs of the crop models, potential yields of grapevines and olive trees were obtained for the recent-past (1981–2005) and for the future (2041–2070), under two climatic scenarios (RCP4.5 and RCP8.5). Results (potential yield) were then normalized, taking into account the recent-past maximum yields and divided into four classes (low, low-moderate, moderate-high, and high). For the recent-past, the results of the crop models present a high agreement with the current location of vineyards and olive groves. For the future, two different types of impacts (positive and negative) are found for the two crops. For olive trees, the results show promising future improvements in possible expansion areas within the Côa region. However, for grapevines, the results show a decrease in potential yields throughout the region, including a strong shift of producing moderate zones to low potentials. Nonetheless, these results also suggest that the negative impacts of climate change can be alleviated by the application of suitable adaptation measures, based on changing certain management practices, even in the more severe future scenario. Therefore, these measures should be carefully planned and implemented in a timely fashion by farmers. [ABSTRACT FROM AUTHOR]

Anticipating sowing date in spring-sown crops is an agronomic strategy often suggested in Europe to escape water and heat stresses during the most susceptible growth periods occurring in early summer. This strategy was evaluated experimentally in soybean (Glycine max (L.) Merrill) by comparing early and conventional sowing dates for a range of cultivars grown in 18 "site x year" environments from southern France between 2010 and 2018. Conventional sowing often resulted in higher grain yields than early sowing, the latter being more favorable to grain size and protein concentration. STICS crop growth model was used to calculate dynamic abiotic (radiation, water, high and low temperatures, nitrogen) stress factors influencing the main crop physiological processes. Ecoclimatic and STICS-based agrophysiological indicators were used in a PLS (Partial Least Squares) regression as explanatory variables of grain yield (r² = 0.33). The key indicators were radiation interception from emergence to maturity, severe stomatal water stress during all the reproductive phase and high temperature since the beginning of grain filling. STICS crop model was used to characterize the 157 "cultivar x site x year x sowing date x irrigation" environments, according to water, heat and cold stresses importance in different phenophases. Then 5 profiles of abiotic stresses emerged from a clustering analysis in two steps by hierarchical ascending classification and K-means. STICS-based environmental characterization was used to explain the relative success or failure of early sowing dates and early-maturing cultivars. ● STICS-based agrophysiological indicators were used to decipher complex G x E x M interactions in soybean. ● The relative performance of early vs. conventional sowing is due to its ability to escape severe water stresses. ● Lower water stress intensity could compensate for reduced crop duration in early soybean cultivars. [ABSTRACT FROM AUTHOR]

• Grain yield of soybean cultivars in southern France was poorly represented by STICS. • A surrogate model was built based on feature selection methods and regression learners. • This model mostly involves STICS-simulated variables related to plant physiology. • The surrogate model strongly improved grain yield predictions from STICS simulations. Empirical and process-based models are currently used to predict crop yield at field and regional levels. A mechanistic model named STICS (Multidisciplinary Simulator for Standard Crops) has been used to simulate soybean grain yield in several environments, including southern France. STICS simulates at a daily step the effects of climate, soil and management practices on plant growth, development and production. In spite of good performances to predict total aboveground biomass, poor results were obtained for final grain yield. In order to improve yield prediction, a surrogate model was developed from STICS dynamic simulations, feature selection techniques and regression learners. STICS was used to simulate functional variables at given growth stages and over selected phenological phases. The most representative variables were selected through feature selection techniques (filter, wrapper and embedded), and a subset of variables were used to train the regression learners Linear regression (LR), Support vector regression (SVR), Back propagation neural network (BPNN), Random forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO) and M5 decision tree. The subset of variables selected by wrapper method combined with regression models SVR (R2 = 0. 7102; subset of variables = 6) and LR (R2 = 0. 6912; subset of variables = 14) provided the best results. SVR and LR models improved significantly the soybean yield predictions in southern France in comparison to STICS simulations (R2 = 0.040). [ABSTRACT FROM AUTHOR]

Abstract: In France, pastures constitute an important land cover type, sustaining principally husbandry production. The absence of low-cost methods applicable to large regions has conducted to the use of simulation models, as in the ISOP system. Remote sensing data may be considered as a potential tool to improve a correct diagnosis in a real time framework. Thirteen forage regions (FR) of France, differing in their soil, climatic and productive characteristics were selected for this purpose. SPOT4-VEGETATION images have been used to provide, using subpixel estimation models, the spectral signature corresponding to pure pasture conditions. This information has been related with some growth variables estimated by STICS-Prairie model (inside ISOP system). Beyond the good general agreement between the two types of data, we found that the best relations were observed between NDVI middle infrared based index (SWVI) and leaf area index. The results confirm the capacities of the satellite data to provide complementary productive variables and help to identify the spatial and temporal differences between satellite and model information, mainly during the harvesting periods. This could contribute to improve the evaluations of the model on a regional scale. [Copyright &y& Elsevier]

• We compared Morris and E-FAST sensitivity analysis methods using three crop models. • Sensitivity analyses were run at sub-model level in different environments. • Results highlighted lack of linearity between total order effects from the methods. • Ranks were instead consistent (p <0.05) in most cases. • We demonstrated the reliability of Morris when ranking is the goal of the analysis. Sensitivity analysis (SA) is a fundamental practice for analyzing model behavior under different conditions of application. A number of SA techniques were proposed, ranging from simple screening methods to computationally expensive variance-based ones. In this study, we compared the Morris and E-FAST methods by applying them to three widely used generic crop models largely differing for complexity and for the approaches used to formalize knowledge on crop physiology, i.e., STICS, CropSyst and WOFOST. SA experiments were carried out at sub-model level on rice crops grown under different environmental conditions. Results highlighted the lack of linearity between the total-order sensitivity estimates provided by E-FAST and Morris, although the concordance (TDCC) between the parameter rankings obtained with the two methods was always significant at the 0.05 level for parameters involved with crop growth and for those involved with phenological development for STICS, whereas it was significant at the 0.10 level for the phenology parameters of CropSyst and WOFOST. Given Morris required less than 3% of the model executions needed by E-FAST, our results allow considering Morris as a suitable alternative to more demanding SA methods when ranking parameters or discriminating between influential and non-influential model factors are the SA goals, especially in computationally expensive SA studies. [ABSTRACT FROM AUTHOR]