Nadine Brisson

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.

How do various maize crop models vary in their responses to climate change factors

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

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.

Coupling canopy functioning and radiative transfer models for remote sensing data assimilation

Abstract Crop functioning models (CFM) are used in many agricultural and environmental applications. Remote sensing data assimilation appears as a good tool to provide more information about canopy state variables in time and space. It permits a reduction in the uncertainties in crop functioning model predictions. This study presents the first step of the assimilation of optical remote sensing data into a crop functioning model. It consists in defining a coupling strategy between well known and validated crop functioning and radiative transfer models (RTM), applied to wheat crops. The radiative transfer model is first adapted to consistently describe wheat, considering of four layers in the canopy that contain different vegetation organs (soil, yellow leaves and senescent stems, green leaves and stems, green and senescent ears). The coupling is then performed through several state variables: leaf area index, leaf chlorophyll content, organ dry matter and relative water content. The relationships between the CFM outputs (agronomic variables) and RTM inputs (biophysical variables) are defined using experimental data sets corresponding to wheat crops under different climatic and stress conditions. The coupling scheme is then tested on the data set provided by the Alpilles–ReSeDA campaign. Results show a good fitting between the simulated reflectance data at top of canopy and the measured ones provided by SPOT images corrected from atmospheric and geometric effects, with a root mean square error lower than 0.05 for all the wavebands.

Including Croplands in a Global Biosphere Model: Methodology and Evaluation at Specific Sites

Abstract There is a strong international demand for quantitative estimates of both carbon sources/sinks, and water availability at the land surface at various spatial scales (regional to global). These estimates can be derived (and usually are) from global biosphere models, which simulate physiological, biogeochemical, and biophysical processes, using a variety of plant functional types. Now, the representation of the large area covered with managed land (e.g., croplands, grasslands) is still rather basic in these models, which were first designed to simulate natural ecosystems, while more and more land is heavily disturbed by man (crops cover ∼35% and grasslands ∼30%–40% of western Europes area as a result of massive deforestation mainly in the Middle Ages). In this paper a methodology is presented that combines the use of a dynamic global vegetation model (DGVM) known as Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and a generic crop model [the Simulateur Multidisciplinaire pour les...

Changes in time of sowing, flowering and maturity of cereals in Europe under climate change

The phenological development of cereal crops from emergence through flowering to maturity is largely controlled by temperature, but also affected by day length and potential physiological stresses. Responses may vary between species and varieties. Climate change will affect the timing of cereal crop development, but exact changes will also depend on changes in varieties as affected by plant breeding and variety choices. This study aimed to assess changes in timing of major phenological stages of cereal crops in Northern and Central Europe under climate change. Records on dates of sowing, flowering, and maturity of wheat, oats and maize were collected from field experiments conducted during the period 1985–2009. Data for spring wheat and spring oats covered latitudes from 46 to 64°N, winter wheat from 46 to 61°N, and maize from 47 to 58°N. The number of observations (site–year–variety combinations) varied with phenological phase, but exceeded 2190, 227, 2076 and 1506 for winter wheat, spring wheat, spring oats and maize, respectively. The data were used to fit simple crop development models, assuming that the duration of the period until flowering depends on temperature and day length for wheat and oats, and on temperature for maize, and that the duration of the period from flowering to maturity in all species depends on temperature only. Species-specific base temperatures were used. Sowing date of spring cereals was estimated using a threshold temperature for the mean air temperature during 10 days prior to sowing. The mean estimated temperature thresholds for sowing were 6.1, 7.1 and 10.1°C for oats, wheat and maize, respectively. For spring oats and wheat the temperature threshold increased with latitude. The effective temperature sums required for both flowering and maturity increased with increasing mean annual temperature of the location, indicating that varieties are well adapted to given conditions. The responses of wheat and oats were largest for the period from flowering to maturity. Changes in timing of cereal phenology by 2040 were assessed for two climate model projections according to the observed dependencies on temperature and day length. The results showed advancements of sowing date of spring cereals by 1–3 weeks depending on climate model and region within Europe. The changes were largest in Northern Europe. Timing of flowering and maturity were projected to advance by 1–3 weeks. The changes were largest for grain maize and smallest for winter wheat, and they were generally largest in the western and northern part of the domain. There were considerable differences in predicted timing of sowing, flowering and maturity between the two climate model projections applied.

Validation of a model of actual evapotranspiration for water stressed soybeans

Abstract In this paper a model for estimating actual evapotranspiration ( ET ) is tested at two experimental sites in Europe (southern Italy and southern France), using data for soybean crops temporarily stressed, grown under a typical Mediterranean climate. The present ET model is based on the Penman-Monteith approach, but uses a canopy r v value that takes account of both stomatal resistance and canopy architecture. In this mode, r v appears only as r v r a , where r a is aerodynamic resistance. r v r a is set to a fraction of the ratio r ∗ r a (with r ∗ called the critical resistance, similar to the isothermal resistance). The function r v r a = f( r ∗ r a ) depends on crop water status as specified by the predawn leaf water potential. The model gives very good results for both sites on hourly, daily and seasonal time scales.

A semiempirical model of bare soil evaporation for crop simulation models

In crop simulation models the water subroutine computes evaporation and transpiration separately. This property allows a greater confidence in the simulation of the early crop stages in terms of water requirements. In the present article the authors suggest a semiempirical model of the drying stage of soil evaporation that can be easily integrated into various crop models. The model is based on a physical approach, yet it depends solely on permanent soil properties and on some key data on the local climate. The basic theory relies upon the mass balance of a dry surface layer of varying thickness. After successive assumptions on the main limiting factors of the drying process, the authors demonstrate that this process can be described by one equation relating actual to potential evaporation. The advantage of this formula comes from its introducing just one synthetic parameter that can be expressed as the product of two components: a climatic one and a pedological one. When compared with experimental data, the results of the model show that the order of magnitude of the evaporative process is respected, which is required for crop 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.

Parameterisation of the Shuttleworth-Wallace model to estimate daily maximum transpiration for use in crop models

In crop models maximum transpiration is an important component of the computation of water stress factors. It depends on reference climatic variables and leaf area index, and also on soil evaporation which modifies the actual air properties around the plants. This last effect is not accounted for in classical approaches used in crop models. Yet Shuttleworth and Wallace theory offers a framework to simulate canopy and soil evaporation fluxes in a coupled way. In this paper an adaptation and a parameterisation of the basic equations from Shuttleworth and Wallace is proposed, allowing use of the model to calculate maximum transpiration by using daily variables. The adaptation concerns soil evaporation. A potential soil evaporation is calculated assuming that, when the soil surface is wet, total evaporative flux consumes the whole available energy. It is used as an input to a two-staged model to calculate actual soil evaporation. The parameterisation relies on two field experiments performed on well-irrigated soybean. Measurements of net radiation balance show that radiation extinction within the canopy is less than generally admitted. Simulations of daily soil evaporation exhibit the same dynamics as microlysimeter measurements, which can be high even when the crop is fully developed. Bulk canopy resistances derived from Bowen ratio measurements agree closely with values obtained from classical formulae using a mean stomatal resistance of 250 ms−1. The modified and properly parameterised model shows that the contribution of plants to total evapotranspiration is highly variable as a result of the interactions between direct soil evaporation and plant transpiration.