Robert Faivre

Predicting Crop Reflectances Using Satellite Data Observing Mixed Pixels

repeatability of the measurements and by the spatial resolution of the sensor: the better the resolution, the less frequent is the observation. To have a regular follow-up, we need to use a satellite with median resolution (around 1 km2 corresponding to a weekly temporal resolution). Pixels with such a resolution correspond to different spatial components (cultures) and are named mixed pixels. We propose a statistical modeling of such satellite data that will enable us to predict information relative to crops observed through mixed pixels. At a given time and restricted to a homogeneous agro-climatic region, this model assumes that reflectances of the same crop (e.g., wheat, barley, and forests) are distributed as Gaussian with parameters depending on the crop. Conditional on the percentage of land occupation, we write a linear model with random parameters. We use the best linear unbiased prediction to predict the individual variations of reflectances. We apply this model to a SPOT image (pixel size 20 x 20 m) with a degraded

Influence of the variation of geometrical and topological traits on light interception efficiency of apple trees: sensitivity analysis and metamodelling for ideotype definition

Background and Aims The impact of a fruit trees architecture on its performance is still under debate, especially with regard to the definition of varietal ideotypes and the selection of architectural traits in breeding programmes. This study aimed at providing proof that a modelling approach can contribute to this debate, by using in silico exploration of different combinations of traits and their consequences on light interception, here considered as one of the key parameters to optimize fruit tree production. Methods The variability of organ geometrical traits, previously described in a bi-parental population, was used to simulate 1- to 5-year-old apple trees (Malus × domestica). Branching sequences along trunks observed during the first year of growth of the same hybrid trees were used to initiate the simulations, and hidden semi-Markov chains previously parameterized were used in subsequent years. Tree total leaf area (TLA) and silhouette to total area ratio (STAR) values were estimated, and a sensitivity analysis was performed, based on a metamodelling approach and a generalized additive model (GAM), to analyse the relative impact of organ geometry and lateral shoot types on STAR. Key Results A larger increase over years in TLA mean and variance was generated by varying branching along trunks than by varying organ geometry, whereas the inverse was observed for STAR, where mean values stabilized from year 3 to year 5. The internode length and leaf area had the highest impact on STAR, whereas long sylleptic shoots had a more significant effect than proleptic shoots. Although the GAM did not account for interactions, the additive effects of the geometrical factors explained >90% of STAR variation, but much less in the case of branching factors. Conclusions This study demonstrates that the proposed modelling approach could contribute to screening architectural traits and their relative impact on tree performance, here viewed through light interception. Even though trait combinations and antagonism will need further investigation, the approach opens up new perspectives for breeding and genetic selection to be assisted by varietal ideotype definition.

Defining and designing plant architectural ideotypes to control epidemics

Ideotypes are a popular concept for plant breeders, who designate as such the ideal combinations of traits in a particular genotype to reach a pre-set production objective within a given socio-economic context. The historical, ‘genetic’ view of ideotypes has been more recently extended to cover the design of plant genotypes for specific cropping systems (the ‘agronomic’ view), or even the ideal combination of parameters, identified from formal or simulation modeling, to a specific agronomic problem (the ‘modelling’ view). These different forms of ideotypes in turn lead to different strategies for breeding plants. This paper will briefly describe, analyse and discuss some applications of these ideotype views, using the specific case of architectural traits of plant and crop canopies to limit the epidemic development of pests and diseases in crops. It is not intended to be an exhaustive and objective review of the existing literature on plant ideotypes, but rather to express as an ‘opinion’ paper the views discussed and elaborated among participants to the EpiArch network.

Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.

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 × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (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 × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification 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.

A Generic Model to Simulate Air-Borne Diseases as a Function of Crop Architecture

In a context of pesticide use reduction, alternatives to chemical-based crop protection strategies are needed to control diseases. Crop and plant architectures can be viewed as levers to control disease outbreaks by affecting microclimate within the canopy or pathogen transmission between plants. Modeling and simulation is a key approach to help analyze the behaviour of such systems where direct observations are difficult and tedious. Modeling permits the joining of concepts from ecophysiology and epidemiology to define structures and functions generic enough to describe a wide range of epidemiological dynamics. Additionally, this conception should minimize computing time by both limiting the complexity and setting an efficient software implementation. In this paper, our aim was to present a model that suited these constraints so it could first be used as a research and teaching tool to promote discussions about epidemic management in cropping systems. The system was modelled as a combination of individual hosts (population of plants or organs) and infectious agents (pathogens) whose contacts are restricted through a network of connections. The system dynamics were described at an individual scale. Additional attention was given to the identification of generic properties of host-pathogen systems to widen the models applicability domain. Two specific pathosystems with contrasted crop architectures were considered: ascochyta blight on pea (homogeneously layered canopy) and potato late blight (lattice of individualized plants). The model behavior was assessed by simulation and sensitivity analysis and these results were discussed against the model ability to discriminate between the defined types of epidemics. Crop traits related to disease avoidance resulting in a low exposure, a slow dispersal or a de-synchronization of plant and pathogen cycles were shown to strongly impact the disease severity at the crop scale.

Varying-time random effects models for longitudinal data: unmixing and temporal interpolation of remote-sensing data

Remote sensing is a helpful tool for crop monitoring or vegetation-growth estimation at a country or regional scale. However, satellite images generally have to cope with a compromise between the time frequency of observations and their resolution (i.e. pixel size). When concerned with high temporal resolution, we have to work with information on the basis of kilometric pixels, named mixed pixels, that represent aggregated responses of multiple land cover. Disaggreggation or unmixing is then necessary to downscale from the square kilometer to the local dynamic of each theme (crop, wood, meadows, etc.). Assuming the land use is known, that is to say the proportion of each theme within each mixed pixel, we propose to address the downscaling issue through the generalization of varying-time regression models for longitudinal data and/or functional data by introducing random individual effects. The estimators are built by expanding the mixed pixels trajectories with B-splines functions and maximizing the log-likelihood with a backfitting-ECME algorithm. A BLUP formula allows then to get the ‘best possible’ estimations of the local temporal responses of each crop when observing mixed pixels trajectories. We show that this model has many potential applications in remote sensing, and an interesting one consists of coupling high and low spatial resolution images in order to perform temporal interpolation of high spatial resolution images (20 m), increasing the knowledge on particular crops in very precise locations. The unmixing and temporal high-resolution interpolation approaches are illustrated on remote-sensing data obtained on the South-Western France during the year 2002.

Shrinkage estimators applied to prediction of French winter wheat yield

SUMMARY The paper shows that it is possible to pool departmental information to predict regional wheat yield. In an MSEP (mean squared error of prediction) context, shrinkage estimation in linear agrometeorological models improves regional prediction. Cross-validation is not precise enough to enable comparison between models. The authors calculate the MSEP using the hypothesis of a multinormal distribution in simple cases (the distribution parameters are estimated with data input) and simulations are used for complex cases. Stein-like estimators are shown to be a superior alternative to the conventional estimators usually applied for prediction.

Using numerical plant models and phenotypic correlation space to design achievable ideotypes: Phenotypic correlations in ideotype design

Simulation models can be used to predict the outcome of plant traits modifications resulting from the genetic variation (and its interaction with the environment) on plant performance, hence gaining momentum in plant breeding process. Optimization methods complement those models in finding ideal values of a set of plant traits, maximizing a defined criteria (e.g. crop yield, light interception). However, using such methods carelessly may lead to misleading solutions, missing the appropriate traits or phenotypes. Therefore, we propose to use domains of potential phenotypes for the search of an optimum, taking into account correlations between traits to ground numerical experiments in biological reality. In addition, we propose a multi-objective optimization formulation using a metric of performance returned by numerical model and a metric of feasibility based on field observations. This can be solved with standard optimization algorithms without any model modification. We applied our approach to two contrasted simulation models: a process-based crop model of sunflower and a structural-functional plant model of apple tree. On both cases, we were able to characterize key plant traits and a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides a proof of concept for this approach and shows that it could improve trait-based breeding methods with paths describing desirable trait modifications both in direction and intensity.Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, that is, ideal values of a set of plant traits, resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of performance criteria (e.g. yield and light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi-objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for correlations between traits based on field observations. We applied this approach to two contrasting models: a process-based crop model of sunflower and a functional-structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modelling approach, which identified paths for desirable trait modification, including direction and intensity.

Designing oil palm architectural ideotypes for optimal light interception and carbon assimilation through a sensitivity analysis of leaf traits

Background and Aims Enhancement of light harvesting in annual crops has successfully led to yield increases since the green revolution. Such an improvement has mainly been achieved by selecting plants with optimal canopy architecture for specific agronomic practices. For perennials such as oil palm, breeding programmes were focused more on fruit yield, but now aim at exploring more complex traits. The aim of the present study is to investigate potential improvements in light interception and carbon assimilation in the study case of oil palm, by manipulating leaf traits and proposing architectural ideotypes. Methods Sensitivity analyses (Morris method and metamodel) were performed on a functional-structural plant model recently developed for oil palm which takes into account genetic variability, in order to virtually assess the impact of plant architecture on light interception efficiency and potential carbon acquisition. Key Results The most sensitive parameters found over plant development were those related to leaf area (rachis length, number of leaflets, leaflet morphology), although fine attributes related to leaf geometry showed increasing influence when the canopy became closed. In adult stands, optimized carbon assimilation was estimated on plants with a leaf area index between 3.2 and 5.5 m2 m-2 (corresponding to usual agronomic conditions), with erect leaves, short rachis and petiole, and high number of leaflets on the rachis. Four architectural ideotypes for carbon assimilation are proposed based on specific combinations of organ dimensions and arrangement that limit mutual shading and optimize light distribution within the plant crown. Conclusions A rapid set-up of leaf area is critical at young age to optimize light interception and subsequently carbon acquisition. At the adult stage, optimization of carbon assimilation could be achieved through specific combinations of architectural traits. The proposition of multiple morphotypes with comparable level of carbon assimilation opens the way to further investigate ideotypes carrying an optimal trade-off between carbon assimilation, plant transpiration and biomass partitioning.