Parameter identification of the STICS crop model, using an accelerated formal MCMC approach

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 modellingPeer reviewe

Development and Evaluation of Plant Growth Models: Methodology and Implementation in the PYGMALION platform

International audienceMathematical models of plant growth are generally characterized by a large number of interacting processes, a large number of model parameters and costly experimental data acquisition. Such complexities make model parameterization a difficult process. Moreover, there is a large variety of models that coexist in the literature with generally an absence of benchmarking between the different approaches and insufficient model evaluation. In this context, this paper aims at enhancing good modelling practices in the plant growth modeling community and at increasing model design efficiency. It gives an overview of the different steps in modelling and specify them in the case of plant growth models specifically regarding their above mentioned characteristics. Different methods allowing to perform these steps are implemented in a dedicated platform PYGMALION (Plant Growth Model Analysis, Identification and Optimization). Some of these methods are original. The C++ platform proposes a framework in which stochastic or deterministic discrete dynamic models can be implemented, and several efficient methods for sensitivity analysis, uncertainty analysis, parameter estimation, model selection or data assimilation can be used for model design, evaluation or application. Finally, a new model, the LNAS model for sugar beet growth, is presented and serves to illustrate how the different methods in PYGMALION can be used for its parameterization, its evaluation and its application to yield prediction. The model is evaluated from real data and is shown to have interesting predictive capacities when coupled with data assimilation techniques

Assessment of the potential impacts of plant traits across environments by combining global sensitivity analysis and dynamic modeling in wheat

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites x 125 years), management practices (3 sowing dates x 2 N fertilization) and $CO_2$ (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait x environment x management landscape ($\sim$ 82 million individual simulations in total). The patterns of parameter x environment x management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identifcation of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.Comment: 22 pages, 8 figures. This work has been submitted to PLoS On

Simultaneous Calibration of Grapevine Phenology and Yield with a Soil–Plant–Atmosphere System Model Using the Frequentist Method

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

Spatial crop-water variations in rainfed wheat systems: From simulation modelling to site-specific management

In sloping fields, rainfed crops experience different degrees of water stress caused by spatial variations in water and, consequently, yields also vary spatially within a field. This offers opportunities for precision agriculture through site-specific management. However, while significant advances have been accomplished in the engineering aspects of precision agriculture, such as increasing spatial resolution of data systems and automation, much less effort has been dedicated to the simulation of within field crop responses to spatial variations. Most studies on rainfed yield gaps ignore intra-plot variability, but if crop models are to be used in assisting site-specific management, they may greatly benefit from spatial water modelling approaches capable of accurately representing and simulating within-field variation of water-related processes. This doctoral thesis represents a novel contribution to the agronomy of rainfed agricultural systems, evaluating the role played by water flows in areas of undulating topography in determining the spatial variations of wheat yield. The thesis has been carried out in chapters that are associated by following an integrative approach. The thesis first reviewed some of the most widely adopted crop and hydrologic models and explored new opportunities for simulating spatial water variations at crop field level through the incorporation of lateral inflow at lower elevation zones of the field. From this standpoint, the spatial variations of yield gaps in rainfed wheat, caused by lateral flows from high to low areas, were assessed in Córdoba, Spain. From an agronomic perspective, water lateral inflows (LIF) due to surface and subsurface runoff contribute to yield variations in rainfed wheat production systems such as the one studied here. The net contribution of these flows to spatial variations of rainfed potential yields showed to be relevant but highly irregular among years. Despite the inter-annual variability, typical of Mediterranean conditions, the occurrence of LIF caused simulated wheat yields to vary +16% from up to downslope areas of the field. Average crop yield ranged from 1.3 to 5.4 Mg grain yield (GY) ha−1. The net yield responses to LIF, in downslope areas were on average 383 kg grain yield (GY) ha−1, and the LIF marginal water productivity reached 24.6 (±13.2) kg GY ha−1 mm−1 in years of maximum responsiveness. Such years of maximum responsiveness were associated with low rainfall during the vegetative stages of the crop in combination with LIF occurring at post-flowering stages. However, under field conditions, these differences were only visible in one of the two experimental years. The economic implications associated with multiple scenarios of variable application rate of nitrogen were explored through a case study and several recommendations were proposed. Both farm size (i.e., annual sown area) and topographic structure impacted the dynamics of investment returns. Under current policy-prices conditions, the adoption of variable application rate would have an economic advantage in farms similar to that of the case study with an annual sown area greater than 567 ha year−1. Nevertheless, current trends on energy prices, transportation costs and impacts on both cereal prices and fertilizers costs enhance the viability of variable application rate adoption for a wider population of farm types. The profitability of adopting VAR improves under such scenarios and, in the absence of additional policy support, the minimum area for adoption of variable application rate decreases to a farm size range of 68-177 ha year−1. The combination of price increases with the introduction of an additional subsidy on crop area could substantially lower the adoption threshold down to 46 ha year−1, turning this technology economically viable for a much wider population of farmers.En campos en pendiente, los cultivos de secano experimentan diferentes grados de estrés hídrico causados por variaciones espaciales de la humedad en el suelo, y los rendimientos varían espacialmente dentro del mismo campo. Esta variabilidad supone una oportunidad para la agricultura de precisión a través del manejo espacialmente variable. Sin embargo, si bien se han logrado avances significativos en los aspectos de la ingeniería de la variación espacial, como el aumento de la resolución espacial de los sistemas de datos y la automatización, se ha avanzado mucho menos en relación a la simulación de las respuestas de los cultivos a las variaciones espaciales de la humedad y los flujos hídricos. La mayoría de los estudios sobre las brechas de rendimiento de secano ignoran la variabilidad dentro de la parcela. Sin embargo, el uso de modelos de simulación de cultivos como medida de apoyo a los sistemas de gestión espacialmente variable, requiere que los enfoques de modelación espacial del agua sean capaces de representar y simular con precisión la variación dentro del campo de los factores relacionados con el agua disponible y la respuesta de los cultivos. Esta tesis doctoral representa una nueva contribución a la agronomía de los sistemas agrícolas de secano, con énfasis en el papel que juegan los flujos de agua en zonas de topografía ondulada en la determinación de las variaciones espaciales del rendimiento del trigo. La tesis se ha desarrollado en capítulos que se complementan siguiendo un enfoque integrador. La presente tesis doctoral revisó algunos de los modelos hidrológicos y de cultivo más ampliamente adoptados y exploró nuevas oportunidades para simular variaciones espaciales del agua a nivel de campo mediante la incorporación del flujo lateral de escorrentía superficial y sub-superficial en las zonas de menor elevación del campo. Desde este punto de vista, se evaluaron las variaciones espaciales de las brechas de rendimiento en trigo de secano, en Córdoba, España, que son causadas por flujos laterales de los puntos altos a los bajos. Desde una perspectiva agronómica, las entradas laterales del agua contribuyen a las variaciones de rendimiento en los sistemas de producción de trigo de secano como el que se ha estudiado en el ámbito de esta tesis. La contribución neta de estos flujos a las variaciones espaciales de los rendimientos potenciales de secano se mostró relevante pero altamente irregular entre diferentes años. A pesar de la variabilidad interanual, típica de las condiciones mediterráneas, la existencia de dichos flujos hizo que los rendimientos de trigo simulados variaran un +16% desde las áreas más elevadas de un campo hacia abajo. El rendimiento medio observado osciló entre 1.3 y 5.4 Mg de rendimiento de grano (GY) ha−1. Las respuestas de rendimiento neto al flujo lateral, cuenca abajo, fueron en promedio 383 kg de rendimiento de grano (GY) ha−1, y la productividad marginal de agua de LIF alcanzó 24.6 (±13.2) kg GY ha−1 mm−1 en años de máxima capacidad de respuesta. Dichos años de máxima capacidad de respuesta se asociaron con bajas precipitaciones durante las etapas vegetativas del cultivo en combinación con flujos laterales en las etapas posteriores a la floración. En condiciones de campo, estas diferencias solo fueron visibles en uno de los dos años experimentales. Las implicaciones económicas asociadas con múltiples escenarios de tasa de aplicación variable de nitrógeno se exploraron a través de un caso de estudio y se propusieron varias recomendaciones. Tanto el tamaño de la finca (el área sembrada anual) como la estructura topográfica afectaron la dinámica de los rendimientos de la inversión. Bajo las condiciones actuales de política agrícola, y de precios, la adopción de la tasa de aplicación variable tendría una ventaja económica en fincas similares a la del caso de estudio con un área sembrada anual superior a 567 ha año−1. Sin embargo, las tendencias actuales en los precios de la energía, los costes de transporte y los impactos tanto en los precios de los cereales como en los costes de los fertilizantes mejoran la viabilidad de la adopción de esta tecnología para una población más amplia de tipos de fincas. La rentabilidad de la adopción de aplicación variable de nitrógeno mejora bajo dichos escenarios y, en ausencia de apoyos adicionales, el área mínima para la adopción de aplicación variable disminuye hasta un rango de 68-177 ha año−1 de área de siembra. La combinación de aumentos de precios con la introducción de un subsidio adicional asociado al área de cultivo podría reducir sustancialmente el umbral de adopción hasta 46 ha año−1, lo que hace que la tecnología sea económicamente viable para una población mucho más amplia de agricultores

Assessing the grapevine crop water stress indicator over the flowering-veraison phase and the potential yield lose rate in important European wine regions

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 (CWSI0.5) 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.This study was funded by Clim4Vitis project-"Climate change impact mitigation for European viticulture: knowledge transfer for an integrated approach", funded by the European Union's Horizon 2020 Research and Innovation Programme, under grant agreement no. 810176; it was also supported by FCT-Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020. We acknowledge the data provisions from members of the PEP725 project, from IPHEN project and from the Consejo Regulador of Ribera de Duero and Rioja DOCa

Modelling the agronomic and environmental impacts of irrigation management on turfgrass for golf greens in northern europe.

Irrigation is an essential component of turfgrass management for golf. During dry periods, it helps maintain turf health, stimulates nitrogen uptake, promotes germination, reduces canopy temperature, as well as assures high standards of quality for playability. In recent years, rising competition for water coupled with new environmental regulations has exerted pressure on water allocations for golf. Improving water efficiency and water management in golf have become major industry priorities. The aim of this thesis was to understand and asses the relationships between irrigation management and turfgrass water use, soil water availability, dry matter production, drainage and nitrate leaching in golf greens under Northern European climate conditions. The research combined published science and industry evidence with field and experimental data, in order to calibrate and validate an irrigation ballistics-based model and a biophysical crop model (STICS). From this, an integrated model (BalliSTICS) was developed and used to simulate the impacts of irrigation uniformity on turfgrass growth and development and leaching risks, under contrasting management and climate scenario. The modelling showed that system design plays a crucial role in achieving high irrigation uniformity, particularly sprinkler position and spacing. A larger spacing between sprinklers resulted in a decrease in irrigation rates and a significant decrease in uniformity, particularly when wind speeds exceeded 2 m s-1. Surprisingly, the range of pressure and nozzle sizes investigated did not significantly impact on irrigation uniformity. Non-uniform irrigation was found to have a considerable impact on the spatial variability in turf growth, soil moisture content, drainage and leaching. Under northern European climate conditions, irrigation strategy had a more significant impact on turfgrass response than irrigation uniformity. A moderate deficit strategy (replacement of 60% potential evapotranspiration) was sufficient to achieve the highest growth values (233 ± 10.6 g m-² season¯¹). This strategy resulted in not only a reduction of irrigation water use but also minimised the amount of nitrate leached in drainage. However, an inadequate irrigation schedule combined with poor irrigation uniformity (CU < 60%) led to a threefold increase in water use, and an average 114% and 50% increase in drainage and nitrate leaching, respectively. Inadequate irrigation practices had little impact on turfgrass growth, which could be misleading as excessive irrigation might not affect plant growth and visual quality but would mask poor irrigation uniformities, lead to excessive water use and an increase in risks of groundwater contamination from leaching. The research provides valuable and novel insights into better understanding the combined impacts of irrigation performance and management on turfgrass. The findings will support greenkeepers and the turfgrass industry and increase awareness of the importance of irrigation

Simulating canopy dynamics, productivity and water balance of annual crops from field to regional scales

2016 Summer.Includes bibliographical references.To provide better understanding of natural processes and predictions for decision support, dynamic models have been used to assess impact of climate, soils and management on crop production, water use, and other responses from field to regional scales. It is important to continue to improve the prediction accuracy and increase the reliability. In this work, we first improved the DayCent ecosystem model by developing a new empirical method for simulating green leaf area index (GLAI) of annual crops. Its performance has been validated using experimental observations from different experimental field locations as well as more aggregate NASS yield data spanning the country. Additionally, sensitivity and uncertainty of important parts of the crop growth model have been quantified. Our results showed the new model provided reliable predictions on crop GLAI, biomass, grain yield, evapotranspiration (ET), and soil water content (SWC) at field scale at various locations. At national scale, the predictions of grain yields were generally accurate with the model capable of representing the geographically-distributed differences in crop yields due to climate, soil, and management. The results indicated that the model is capable of providing insightful predictions for use in management and policy decision making. Although there are challenges to be addressed, our results indicate that the DayCent model can be a valuable tool to assess crop yield changes and other agroecosystem processes under scenarios of climate change in the future

Crop modeling for assessing and mitigating the impacts of extreme climatic events on the US agriculture system

The US agriculture system is the world’s largest producer of maize and soybean, and typically supplies more than one-third of their global trading. Nearly 90% of the US maize and soybean production is rainfed, thus is susceptible to climate change stressors such as heat waves and droughts. Process-based crop and cropping system models are important tools for climate change impact assessments and risk management. As data- science is becoming a new frontier for agriculture growth, the incoming decade calls for operational platforms that use hyper-local growth monitoring, high-resolution real-time weather and satellite data assimilation and cropping system modeling to help stakeholders predict crop yields and make decisions at various spatial scales. The fundamental question addressed by this dissertation is: How crop and cropping system models can be “useful” to the agriculture production, given the recent advent of cloud computing and earth observatory power? This dissertation consists of four main chapters. It starts with a study that reviews the algorithms of simulating heat and drought stress on maize in 16 major crop models, and evaluates algorithm performances by incorporating these algorithms into the Agricultural Production Systems sIMulator (APSIM) and running an ensemble of simulations at typical farms from the US Midwest. Results show that current parameterizations in most models favor the use of daylight temperature even though the algorithm was designed for using daily mean temperature. Different drought algorithms considerably differed in their patterns of water shortage over the growing season, but nonetheless predicted similar decreases in annual yield. In the next chapter of climate change assessment study, I quantify the current and future yield responses of US rainfed maize and soybean to climate extremes with and without considering the effect of elevated atmospheric CO2concentrations, and for the first time characterizes spatial shifts in the relative importance of temperature, heat and drought stresses. Model simulations demonstrate that drought will continue to be the largest threat to rainfed maize and soybean production, yet shifts in the spatial pattern of dominant stressors are characterized by increases in the concurrent stress, indicating future adaptation strategies will have trade-offs between multiple objectives. Following this chapter, I presented a chapter that uses billion-scale simulations to identify the optimal combination of Genotype × Environment × Management for the purpose of minimizing the negative impact of climate extremes on the rainfed maize yield. Finally, I present a prototype of crop model and satellite imagery based within-field scale N sidedress prescription tool for the US rainfed maize system. As an early attempt to integrate advances in multiple areas for precision agriculture, this tool successfully captures the subfield variability of N dynamics and gives reasonable spatially explicit sidedress N recommendations. The prescription enhances zones with high yield potentials, while prevents over-fertilization at zones with low yield potentials

Sugarbeet Model Development for Soil and Water Quality Assessment

Sugarbeet (Beta vulgaris) is considered as one of the most viable alternatives to corn for biofuel production as it may be qualified as “advanced” biofuel feedstocks under the ‘EISA 2007’. Production of deep rooted sugarbeet may play a significant role in enhancing utilization of deeper layer soil water and nutrients, and thus may significantly affect soil health and water quality through recycling of water and nutrients. A model can be useful in predicting the sugarbeet growth, and its effect on soil and water quality. A sugarbeet model was developed by adopting and modifying the Crop Environment and Resource Synthesis-Beet (CERES-Beet) model. It was linked to the Cropping System Model (CSM) of the Decision Support System for Agrotechnology (DSSAT) and was termed as CSM-CERES-Beet. The CSM-CERES-Beet model was then linked to the plant growth module of the Root Zone Water Quality Model (RZWQM2) to simulate crop growth, soil water and NO3-N transport in crop fields. For both DSSAT and RZWQM2, parameter estimation (PEST) software was used for model calibration, evaluation, predictive uncertainty analysis, sensitivity, and identifiability. The DSSAT model was evaluated with two sets of experimental data collected in two different regions and under different environmental conditions, one in Bucharest, Romania and the other in Carrington, ND, USA, while RZWQM2 was evaluated for only Carrington, ND experimental data. Both DSSAT and RZWQM2 performed well in simulating leaf area index, leaf or top weight, and root weight for the datasets used (d-statistic = 0.783-0.993, rRMSE = 0.006-1.014). RZWQM2 was also used to evaluate soil water and NO3-N contents and did well (d-statistic = 0.709-0.992, rRMSE = 0.066-1.211). The RZWQM2 was applied for simulating the effects of crop rotation and tillage operations on sugarbeet production. Hypothetical crop rotation and tillage operation scenarios identified wheat as the most suitable previous year crop for sugarbeet and moldboard plow as the most suitable tillage operation method. Both DSSAT and RZWQM2 enhanced with CSM-CERES-Beet may be used to simulate sugarbeet production under different management scenarios for different soils and under different climatic conditions in the Red River Valley.USDA National Institute of Food and Agriculture Foundational Program (Award No.: 2013-67020-21366