Добірка наукової літератури з теми "STICS soil-crop model"

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.

Inversing the STICS crop model with remote-sensing-derived leaf area index (LAI) and yield data from the previous crop is used to retrieve some soil permanent properties and crop emergence parameters. Spatialized nitrogen (N) fertilization recommendations are provided to farmers, for the second and third N applications, following the screening of eleven N application rates under a range of possible forthcoming climates, with the objective to maximize of the gross margin while respecting some environmental constraints. As a first field validation, we show (1) the improvement brought by the assimilation of LAI and yield into STICS to simulate crop and soil variables and (2) the interest of site specific application to maximize both the gross margin and the agro-environmental criterion.

<p style="text-align: justify;"><strong>Background and aims</strong>: Many models have been developed to evaluate crop growth and development, but few are capable of simulating grapevine systems. The present study was carried out to evaluate the ability of the STICS model to represent grapevine phenology, biomass production, yield and soil water content in two situations differing with respect to rainfall distribution and water management strategies.</p><p style="text-align: justify;"><strong>Methods and results</strong>: Simulations were performed for an irrigated vineyard in Chile and an irrigated and a non-irrigated vineyard in France. The crop model gave a good estimation of the main stages of grapevine phenology (less than six days difference between simulated and observed values). Soil water content was the best simulated variable (R2 = 0.99), whereas grapevine evapotranspiration observed only in Chile (R2 = 0.43) and leaf area index observed only in France (R2= 0.80) were the worst simulated variables. Biomass production, yield and their components were correctly simulated (within the 95 % Student confidence interval around the mean observed value). A comparison of the fraction of transpirable soil water and vine water potential measurements with the water stress indices calculated by the STICS model showed that the time and duration of the grapevine water stress period was correctly estimated.</p><p style="text-align: justify;"><strong>Conclusions</strong>: Therefore, the STICS model was reasonably successful in simulating vine growth and development, and identifying critical periods concerning the vine water status.</p><p style="text-align: justify;"><strong>Significance of the study</strong>: The STICS model can be used to evaluate various water management strategies and their impacts on grape production.</p>

Abstract. Agro-land surface models (agro-LSM) have been developed from the integration of specific crop processes into large-scale generic land surface models that allow calculating the spatial distribution and variability of energy, water and carbon fluxes within the soil–vegetation–atmosphere continuum. When developing agro-LSM models, particular attention must be given to the effects of crop phenology and management on the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty of agro-LSM models is related to their usually large number of parameters. In this study, we quantify the parameter-values uncertainty in the simulation of sugarcane biomass production with the agro-LSM ORCHIDEE–STICS, using a multi-regional approach with data from sites in Australia, La Réunion and Brazil. In ORCHIDEE–STICS, two models are chained: STICS, an agronomy model that calculates phenology and management, and ORCHIDEE, a land surface model that calculates biomass and other ecosystem variables forced by STICS phenology. First, the parameters that dominate the uncertainty of simulated biomass at harvest date are determined through a screening of 67 different parameters of both STICS and ORCHIDEE on a multi-site basis. Secondly, the uncertainty of harvested biomass attributable to those most sensitive parameters is quantified and specifically attributed to either STICS (phenology, management) or to ORCHIDEE (other ecosystem variables including biomass) through distinct Monte Carlo runs. The uncertainty on parameter values is constrained using observations by calibrating the model independently at seven sites. In a third step, a sensitivity analysis is carried out by varying the most sensitive parameters to investigate their effects at continental scale. A Monte Carlo sampling method associated with the calculation of partial ranked correlation coefficients is used to quantify the sensitivity of harvested biomass to input parameters on a continental scale across the large regions of intensive sugarcane cultivation in Australia and Brazil. The ten parameters driving most of the uncertainty in the ORCHIDEE–STICS modeled biomass at the 7 sites are identified by the screening procedure. We found that the 10 most sensitive parameters control phenology (maximum rate of increase of LAI) and root uptake of water and nitrogen (root profile and root growth rate, nitrogen stress threshold) in STICS, and photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), and transpiration and respiration (stomatal conductance, growth and maintenance respiration coefficients) in ORCHIDEE. We find that the optimal carboxylation rate and photosynthesis temperature parameters contribute most to the uncertainty in harvested biomass simulations at site scale. The spatial variation of the ranked correlation between input parameters and modeled biomass at harvest is well explained by rain and temperature drivers, suggesting different climate-mediated sensitivities of modeled sugarcane yield to the model parameters, for Australia and Brazil. This study reveals the spatial and temporal patterns of uncertainty variability for a highly parameterized agro-LSM and calls for more systematic uncertainty analyses of such models.

Abstract. Agro-Land Surface Models (agro-LSM) have been developed from the integration of specific crop processes into large-scale generic land surface models that allow calculating the spatial distribution and variability of energy, water and carbon fluxes within the soil-vegetation-atmosphere continuum. When developing agro-LSM models, a particular attention must be given to the effects of crop phenology and management on the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty of Agro-LSM models is related to their usually large number of parameters. In this study, we quantify the parameter-values uncertainty in the simulation of sugar cane biomass production with the agro-LSM ORCHIDEE-STICS, using a multi-regional approach with data from sites in Australia, La Réunion and Brazil. In ORCHIDEE-STICS, two models are chained: STICS, an agronomy model that calculates phenology and management, and ORCHIDEE, a land surface model that calculates biomass and other ecosystem variables forced by STICS' phenology. First, the parameters that dominate the uncertainty of simulated biomass at harvest date are determined through a screening of 67 different parameters of both STICS and ORCHIDEE on a multi-site basis. Secondly, the uncertainty of harvested biomass attributable to those most sensitive parameters is quantified and specifically attributed to either STICS (phenology, management) or to ORCHIDEE (other ecosystem variables including biomass) through distinct Monte-Carlo runs. The uncertainty on parameter values is constrained using observations by calibrating the model independently at seven sites. In a third step, a sensitivity analysis is carried out by varying the most sensitive parameters to investigate their effects at continental scale. A Monte-Carlo sampling method associated with the calculation of Partial Ranked Correlation Coefficients is used to quantify the sensitivity of harvested biomass to input parameters on a continental scale across the large regions of intensive sugar cane cultivation in Australia and Brazil. Ten parameters driving most of the uncertainty in the ORCHIDEE-STICS modeled biomass at the 7 sites are identified by the screening procedure. We found that the 10 most sensitive parameters control phenology (maximum rate of increase of LAI) and root uptake of water and nitrogen (root profile and root growth rate, nitrogen stress threshold) in STICS, and photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), and transpiration and respiration (stomatal conductance, growth and maintenance respiration coefficients) in ORCHIDEE. We find that the optimal carboxylation rate and photosynthesis temperature parameters contribute most to the uncertainty in harvested biomass simulations at site scale. The spatial variation of the ranked correlation between input parameters and modeled biomass at harvest is well explained by rain and temperature drivers, suggesting climate-mediated different sensitivities of modeled sugar cane yield to the model parameters, for Australia and Brazil. This study reveals the spatial and temporal patterns of uncertainty variability for a highly parameterized agro-LSM and calls for more systematic uncertainty analyses of such models.

L’orge de printemps (Hordeum vulgare L.) et la fléole des prés (Phleum pratense L.) sont des cultures de première importance économique pour la Province du Québec et d’autres régions de climat continental froid et humide (Amérique du Nord, les pays nordiques…). Les modèles sol-culture sont des outils puissants capables de calculer de nombreuses variables d’intérêt agronomique et environnemental. Ils sont conçus pour simuler les interactions complexes entre les cultures, l'eau et l'azote (N) du sol dans le continuum sol–plante–atmosphère. Entre autres modèles, STICS est un modèle sol–culture basé sur les processus, qui a été développé initialement pour des conditions agropédoclimatiques de régions tempérées. Cependant, étant un modèle générique, il est possible de l’adapter aux conditions d’autres agrosystèmes.Les objectifs de cette thèse étaient d’analyser et d’élargir le domaine d’application de STICS à ces deux cultures d’importance économique cultivées dans des conditions agropédoclimatiques de la province de Québec et d’évaluer les performances prédictives du modèle sur des simulations sur le long terme. Cette thèse est une contribution à l’étude de la généricité de STICS pour des agrosystèmes québécois. Outre le contexte climatique, l’originalité de ce travail porte sur les cultures étudiées, orge de printemps et fléole des prés, et le nombre d’années successives de simulations en continu (sans réinitialisation annuelle). Les performances prédictives de STICS ont été analysées pour la production de biomasse aérienne annuelle, sa teneur en N et la quantité de N exporté pour i) une monoculture d’orge de printemps de 31 ans cultivée avec deux modes de travail du sol et fertilisée avec deux sources de N différentes (engrais azoté minéral et lisier de vaches laitières) ; et ii) une prairie de fléole des prés de 8 ans, fertilisée chaque année avec quatre doses d’engrais azoté minéral (0, 60, 120, 180 kg N ha-1). Nous avons utilisé les bases de données de deux dispositifs expérimentaux au champ d’Agriculture et Agroalimentaire Canada.Pour la monoculture d’orge, la procédure de calibration de STICS a nécessité l'ajustement des paramètres de cultivar en particulier, confirmant ainsi la généricité de la plupart des paramètres des plantes définis dans STICS. Les valeurs simulées sur une période de 31 ans se sont révélées être correctement en accord avec les valeurs observées des variables d’intérêt pour les différents traitements, mais avec une plus grande dispersion pour la nutrition azotée. Les résultats de la simulation des attributs de la production végétale au moment de la récolte étaient plus précis pour les années où les précipitations étaient proches de la normale. Pour la prairie de fléole des prés suivie pendant 8 ans, la correspondance entre les valeurs observées et simulées était satisfaisante pour la première coupe effectuée au printemps. STICS a correctement simulé l'effet positif de la dose de fertilisation azotée sur la production de biomasse et la nutrition azotée des plantes. Néanmoins, les valeurs simulées étaient surestimées par le modèle en l’absence de fertilisation azotée. Si l’on excepte cette situation très particulière, non représentative des pratiques agronomiques, les performances de STICS sont donc satisfaisantes dans le contexte dans le contexte des deux essais au champ étudiés. De plus, STICS a bien reproduit la tendance à la baisse de la productivité de la fléole des prés observée en fonction de l'âge de la prairie. Les résultats ont montré que cette baisse de rendement au fil du temps est fortement corrélée à la réduction de la réserve métabolique dans les organes de réserve.En conclusion, ce travail de thèse a montré l’applicabilité et la fiabilité du modèle STICS pour la simulation sur le long terme de la production de biomasse et de la nutrition azotée d'orge de printemps et de la fléole des prés dans des conditions agropédoclimatiques de la Province de Québec
Spring barley (Hordeum vulgare L.) and timothy (Phleum pratense L.) are crops of prime economic importance for the province of Quebec and other regions with cold, humid continental climate (North America, Nordic countries, etc.). Soil-crop models are powerful tools for calculating, a wide range of agronomic and environmental variables, since they are designed to simulate the complex interactions between crops, water, and soil nitrogen (N) in the soil–plant–atmosphere continuum. Among other models, STICS is a process-based soil-crop model developed initially for temperate agropedoclimatic conditions. However, it can be adapted to conditions of other agrosystems.The objectives of this thesis were to analyze and extend the scope of application of STICS to these two economically important crops grown under agropedoclimatic conditions in the Province of Quebec, and to evaluate the model's predictive performance on long–term simulations. This thesis is a contribution to the study of the genericity of STICS for Quebec agrosystems. In addition to the climatic context, the originality of this work lies in the crops studied – spring barley and timothy – and the number of successive years of continuous simulations (without annual reinitialization). The predictive performances of STICS were analyzed for aboveground biomass production, N content and N export for i) a 31-year spring barley monoculture grown under two tillage systems and fertilized with two N sources (mineral N and liquid dairy manure); and ii) an 8-year timothy grassland, fertilized each year with four application N rates (0, 60, 120, 180 kg N ha-1). We used databases from two experimental field trials conducted by Agriculture and Agri-Food Canada.For the barley monoculture, the STICS calibration procedure required the adjustment of cultivar parameters in particular, thus confirming the genericity of most plant parameters defined in STICS. There is a good agreement between observed and predicted variables of interest with the various tillage systems and N sources during the 31 successive barley cropping years, but with greater dispersion for the N nutrition. Predictions of crop attributes were more accurate in years with rainfall close to the long-term average. For timothy grassland grown over 8 years, the agreement between observed and predicted values was satisfactory for the first harvest. STICS correctly simulated the positive effect of the N application rates on biomass production and plant N nutrition. Nevertheless, the predicted values were overestimated by the model in the absence of N fertilization. Except for this very specific situation, which is not representative of agronomic practices, STICS performed satisfactorily in the context of the two field experiments studied. In addition, STICS reproduced well decreasing trend in timothy productivity observed with the age of the sward. The results showed that this decrease in yield over time is strongly correlated with the reduction in metabolic reserve in the perennial organs.In conclusion, this thesis work has demonstrated the applicability and reliability of the STICS model for the long-term simulation of biomass production and N nutrition of spring barley and timothy under agropedoclimatic conditions in the Province of Quebec

Les modèles de culture constituent des outils indispensables pour comprendre l’influence des conditions agropédoclimatiques sur le système sol-plante à différentes échelles spatiales et temporelles. A l’échelle locale de la parcelle agricole, le modèle peut être utilisé dans le cadre de l’agriculture de précision pour optimiser les pratiques de fertilisation azotée de façon à maximiser le rendement ou le revenu tout en minimisant le lessivage des nitrates vers la nappe. Cependant, la pertinence de l’utilisation du modèle repose sur la qualité des prédictions réalisées, basée entre autres sur une bonne détermination des paramètres d’entrée du modèle. Dans le cadre de l’agriculture de précision, les paramètres concernant les propriétés des sols sont les plus délicates à connaître en tout point de la parcelle et il existe très peu de cartes de sols permettant de les déterminer de manière précise. Néanmoins, dans ce contexte, on peut disposer d’observations acquises automatiquement sur l’état du système sol-plante, telles que des images de télédétection, les cartes de rendement ou les mesures de résistivité électrique du sol. Il existe alors une alternative intéressante pour estimer les propriétés des sols à l’échelle de la parcelle qui consiste à inverser le modèle de culture à partir de ces observations pour retrouver les valeurs des propriétés des sols. L’objectif de cette thèse consiste (i) dans un premier temps à analyser les performances d’estimation des propriétés des sols par inversion du modèle STICS à partir de différents jeux d’observations sur des cultures de blé et de betterave sucrière, en mettant en oeuvre une méthode bayésienne de type Importance Sampling, (ii) dans un second temps à mesurer l’amélioration des prédictions de variables agro-environnementales réalisées par le modèle à partir des valeurs estimées des paramètres. Nous montrons que l’analyse de sensibilité globale permet de quantifier la quantité d’information contenue dans les jeux d’observations et les performances réalisées en matière d’estimation des paramètres. Ce sont les propriétés liées au fonctionnement hydrique du sol (humidité à la capacité au champ, profondeur de sol, conditions initiales) qui bénéficient globalement de la meilleure performance d’estimation par inversion. La performance d’estimation, évaluée par comparaison avec l’estimation fournie par l’information a priori, dépend fortement du jeu d’observation et est significativement améliorée lorsque les observations sont faites sur une culture de betterave, les conditions climatiques sont sèches ou la profondeur de sol est faible. Les prédictions agro-environnementales, notamment la quantité et la qualité du rendement, peuvent être grandement améliorées lorsque les propriétés du sol sont estimées par inversion, car les variables prédites par le modèle sont également sensibles aux propriétés liées à l’état hydrique du sol. Pour finir, nous montrons dans un travail exploratoire que la prise en compte d’une information sur la structure spatiale des propriétés du sol fournie par les mesures de résistivité électrique, peut permettre d’améliorer l’estimation spatialisée des propriétés du sol. Les observations acquises automatiquement sur le couvert végétal et la résistivité électrique du sol se révèlent être pertinentes pour estimer les propriétés du sol par inversion du modèle et améliorer les prédictions des variables agro-environnementales sur lesquelles reposent les règles de choix des pratiques agricoles
Dynamic crop models are very useful to predict the behavior of crops in their environment and are widely used in a lot of agro-environmental work. These models have many parameters and their spatial application require a good knowledge of these parameters,especially of the soil parameters. These parameters can be estimated from soil analysis at different points but this is very costly and requires a lot of experimental work. Nevertheless,observations on crops provided by new techniques like remote sensing or yield monitoring, is a possibility for estimating soil parameters through the inversion of crop models. In my work, the STICS crop model is studied for the wheat and the sugar beet and it includes more than 200 parameters. After a previous work based on a large experimental database for calibrate parameters related to the characteristics of the crop, I started my study with a global sensitivity analysis of the observed variables (leaf area index LAI and absorbed nitrogen QN provided by remote sensing data, and yield at harvest provided by yield monitoring) to the soil parameters, in order to determine which of them have to be estimated. This study was made in different climatic and agronomic conditions and it reveals that 7 soil parameters (4 related to the water and 3 related to the nitrogen) have a clearly influence on the variance of the observed variables and have to be therefore estimated. For estimating these 7 soil parameters, I chose a Bayesian data assimilation method (because I have prior information on these parameters) named Importance Sampling by using observations, on wheat and sugar beet crop, of LAI and QN at various dates and yield at harvest acquired on different climatic and agronomic conditions. The quality of parameter estimation is then determined by comparing the result of parameter estimation with only prio rinformation and the result with the posterior information provided by the Bayesian data assimilation method. The result of the parameter estimation show that the whole set of parameter has a better quality of estimation when observations on sugar beet are assimilated. At the same time, global sensitivity analysis of the observed variables to the 7 soil parameters have been performed, allowing me to build a criterion based on sensitivity indices (provided by the global sensitivity analysis) able to rank the parameters with respect to their quality of estimate. This criterion constitutes an interesting tool for determining which parameters it is possible to estimate to reduce probably the uncertainties on the predictions. The prediction of the crop behaviour when estimating the soil parameters is then studied. Indeed, the quality of prediction of agro-environmental variables of the STICS crop model (yield, protein of the grain and nitrogen balance at harvest) is determined by comparing the result of the prediction using the prior information on the parameters and the result using the posterior information. As for the estimation of soil parameters, the prediction of the variable is made on different climatic and agronomic conditions. According to the result of parameter estimation, assimilating observations on sugar beet lead to a better quality ofprediction of the variables than observations on wheat. It was also shown that the number ofcrop seasons observed and the number of observations improve the quality of the prediction

One of the main challenges in the transition to more sustainable agriculture is designing and selecting agricultural systems that are stable and perturbation resistant. Crop diversification is now recognized as a decisive part of sustainable agroecological development. It is one of the crucial agroecological practices that prove ecosystem services such as nutrient cycling, biological N fixation, pest and disease regulation, erosion control, climate regulation, soil fertility maintenance, biodiversity conservation, and carbon sequestration. To maximize these desired outcomes, understanding, designing, and optimizing, the adoption of crop diversification is crucial for the sustainability of food production under low-input practices. One approach to building sustainable food security and optimal management systems for limited resources is through the application of crop simulation models in multi-cropping systems. Indeed, some models can be used to simulate intercropping systems such as DSSAT, APSIM, ALMANAC, STICS, and FASSET. Thus, the application of such powerful models provides an option to redesign crop mixtures in appropriate sowing proportion and sowing date to tackle the enormous challenges facing agricultural development. In this regard, this review intended to assess existing suitable model to simulate multiple cropping systems and its role in building resilient crop production and ecosystem services without damaging the environment. It also highlights the key role of crop diversity as an ecosystem service provider to guarantee plant productivity in emerging systems of sustainable agriculture.