Patrick Bertuzzi

An overview of the crop model stics

Abstract stics is a model that has been developed at INRA (France) since 1996. It simulates crop growth as well as soil water and nitrogen balances driven by daily climatic data. It calculates both agricultural variables (yield, input consumption) and environmental variables (water and nitrogen losses). From a conceptual point of view, stics relies essentially on well-known relationships or on simplifications of existing models. One of the key elements of stics is its adaptability to various crops. This is achieved by the use of generic parameters relevant for most crops and on options in the model formalisations concerning both physiology and management, that have to be chosen for each crop. All the users of the model form a group that participates in making the model and the software evolve, because stics is not a fixed model but rather an interactive modelling platform. This article presents version 5.0 by giving details on the model formalisations concerning shoot ecophysiology, soil functioning in interaction with roots, and relationships between crop management and the soil–crop system. The data required to run the model relate to climate, soil (water and nitrogen initial profiles and permanent soil features) and crop management. The species and varietal parameters are provided by the specialists of each species. The data required to validate the model relate to the agronomic or environmental outputs at the end of the cropping season. Some examples of validation and application are given, demonstrating the generality of the stics model and its ability to adapt to a wide range of agro-environmental issues. Finally, the conceptual limits of the model are discussed.

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Agrometeorological soil water balance for crop simulation models

Abstract The use of crop simulation models on a large scale for agrometeorological purposes is often limited by their inputs being non-routinely collected data, especially with regard to their soil water balance compartment. The objective of this study is to develop a water balance sub-model which can be run with readily available inputs. The model predicts water use, soil evaporation and crop transpiration throughout the growing season. Physiological reduction factors, as influenced by water stress, are derived from the soil water availability. This is achieved by using empirical relationships such as the reservoir analogy to assess water availability in relation to root development. The framework of applicable conditions is assessed by sensitivity analyses performed on inputs: the model can be run with a time step of ten days and using soil information given by soil maps, i.e. soil texture and depth, which fit agrometeorological purposes. Moreover, the model is shown to describe realistically soil water depletion, crop evapotranspiration and rooting depth. However, wetting processes are not correctly simulated, especially when large amounts of water are supplied. This limitation is emphasized for ten day time steps. Therefore incorporating effective rainfall simulation, i.e. both runoff and rainfall interception by the canopy, would improve the model.

Estimating root zone soil moisture from surface soil moisture data and soil-vegetation-atmosphere transfer modeling

We studied the possibility of estimating root zone soil moisture through the combined use of a time series of observed surface soil moisture data and soil-vegetation-atmosphere transfer modeling. The analysis was based on the interactions between soil- biosphere-atmosphere surface scheme and two data sets obtained from soybean crops in 1989 and 1990. These data sets included detailed measurements of soil and vegetation characteristics and mass and energy transfer in the soil-plant-atmosphere system. The data measured during the 3-month experiment in 1989 are used to investigate the accuracy of soil reservoir retrievals, as a function of the time period and frequency of measurements of surface soil moisture involved in the retrieval process. This study contributes to better defining the requirements for the use of remotely sensed microwave measurements of surface soil moisture.

A simple approach to monitor crop biomass from C-band radar data

Abstract A simple two-term model of the radar backscattering coefficient of crops, designed for the retrieval of the amount of water in the canopy, is described and analyzed. The principle of the method is to calibrate the simple model from the simulations of a discrete first-order radiative transfer model during crop development. The canopy structure is taken into account in the discrete model to compute the relationships between a) the vegetation direct contribution to backscattering σ ° v and the optical depth τ and b) the optical depth τ and the amount of water in the canopy. The two-term model is tested against C-band radar data acquired over a soybean crop during the whole vegetation cycle. The simulations correlate well with the measurements and the retrieval of the amount of water in the canopy Wc (kg/m 2 ) can be carried out. Accurate temporal information on the crop growth could be derived from the radar data. Ancillary information about soil moisture are required, but it is found that rough estimates on a 4–5 day basis are sufficient.

A parametric study on passive and active microwave observations over a soybean crop

This work investigates the potential use of passive and active microwave observations to monitor soil moisture and vegetation biomass over a soybean cover. The work is based on a sensitivity analysis from a large set of data generated by radiative-transfer models. Some appropriate configurations are identified. In particular, the combined use of passive data at 1.4 GHz, with multiangle active measurements at 5 GHz, is found to be promising.

Estimation of energy fluxes from thermal infrared, spectral reflectances, microwave data and SVAT modeling

Abstract The ALiBi model, a Soil-Vegetation-Atmosphere Transfer model, was developed to simulate mass and energy exchanges between vegetation canopies, the soil and the atmosphere. In the present work, it was used in conjunction with remote sensing data in thermal, solar and microwave domains to assess energy balance of various vegetation canopies (two soybean crops, one wheat crop and one fallow canopy). Inversions of radiative transfer formulations were performed to retrieve model parameters: -i) canopy structure parameters (leaf area index and canopy height) from red and near-infrared reflectances, when available; -ii) surface soil moisture from microwave measurements, when available and -iii) plant water transfer parameters from thermal infrared measurements. The estimates of energy balance fluxes were in good agreement with measured values, in particular for well-developed canopies. It can be noticed that it was not necessary to know accurately surface soil moisture and leaf area index to assess energy balance fluxes. Conversely, the vegetation height must be known with a good accuracy.

Sampling strategies for soil water content to estimate evapotranspiration

When the soil water balance method is applied at a field scale, estimation of the spatial variability and confidence interval of actual evapotranspiration is rare, although this method is sensitive to the spatial variability of the soil, and thus to the sampling strategy. This work evaluated the effect of soil sampling strategies for soil water content and water flux at the bottom of the soil profile on the estimation of the daily and cumulative evapotranspirations. To do that, according to the statistical properties of daily measurements in a field experiment with a soybean crop, the water content and flux through the base to the soil profile in space (field scale) and time (daily scale) were simulated. Four different sampling strategies were then compared, and their effects on daily and seasonal cumulative evapotranspirations quantified. Strategy 1 used ten theoretical sites randomly located in the field. The daily water content estimates were assumed to be available each day from these same ten locations, which were located from 0.15 m to 1.55 m in depth, with space steps of 0.10 m. Strategy 2 assumed that daily water content estimates combined two sources: in the 0.00–0.20 m soil layer, ten theoretical sites were selected but changed every day, with thin soil layers for soil moisture sampling, from 1 to 5 cm in thickness. In the 0.20–1.60 m soil layer, the daily water content estimates were assumed to come from the same ten locations (the first soil moisture estimate was located at 0.25 m, and the others were located every 0.10 m until 1.55 m). Strategy 3 used ten theoretical sites located in the field, as in strategy 1, however the water content estimates in the 0.00–0.20-m soil layer were assumed to come from accurate water content measurements (soil layers from 1 to 5 cm in thickness), while for the 0.20–1.60 m soil layer, the strategy was similar to strategies 1 and 2. Strategy 4 used 10 new theoretical locations of measurement every day. Precise water content estimates for thin layers were assumed to be available in the 0.00–0.20 m soil layer as in strategy 2. The layers for water content estimates in the 0.20–1.60 m were similar to those of strategies 1, 2, and 3. Results showed that the spatial variability of the daily actual evapotranspiration may not be negligible, and differences from approximately ±1.0 mm d −1 to ±3.0 mm d −1 were calculated between the four sampling strategies. Strategy 1 gave the worst results, because variations in the water content of the top soil layers were neglected, and thus the daily evapotranspiration was underestimated. Strategy 2 led to a considerable variability for estimating daily evapotranspiration which was explained by the effect of the spatial variability due to the daily site sampling for the top soil layers (0 to 0.2 m). Strategy 3 appeared to be the best practical compromise between practical field considerations and the necessity to obtain accurate evapotranspiration measurements. The accuracy of daily evapotranspiration could reach ± 0.5 mm d−1, and could be further improved by increasing the number of measurement sites. The best results were obtained with strategy 4, although such a destructive and time-consuming strategy is not likely to be practical.

The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, andwater (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models’ responses to CTW changes (R¨otter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012)...

Estimation of energy fluxes and photosynthesis from thermal infrared, spectral reflectances, microwave data and SVAT modeling

Soil Vegetation Atmosphere Transfer (SVAT) models have been implemented to simulate energy and mass fluxes between soil, vegetation and atmosphere of various ecosystems. Usually, these models are simple, but they use realistic descriptions of radiative, turbulent and water transfers. These include description of stomatal control of transpiration and CO/sub 2/ fluxes. They can be used for assimilating remote sensing data and derive vegetation canopy evapotranspiration or photosynthesis. Various remote sensing data may provide useful information to drive SVAT models. Surface temperature may be used through inversion procedures to retrieve parameters related to stomatal conductance or root zone soil moisture. Parameters related to vegetation structure (LAI, vegetation height) may be retrieved from reflectance measurements in the solar domain, either through direct relationships with some vegetation index or by inverting radiative transfer formulation against spectral reflectance measurements. The microwave data contribution has not been studied very often in the case of vegetation canopies, but they were proposed for estimating surface soil moisture. In this paper, inversions of the ALiBi model were performed to retrieve canopy evapotranspiration from thermal infrared, spectral reflectances and microwave data on two water stressed soybean crops. In a previous study, thermal infrared data alone were used to invert the model on plant water status parameters, while other parameters, related to canopy structure and soil surface water status, were prescribed from in situ measurements. In the present study, spectral reflectance and radar measurements were used to retrieve canopy structure parameters (LAI and vegetation height) and surface soil moisture by inverting radiative transfer models.