Marie Launay

Evaluation of the impact of various agricultural practices on nitrate leaching under the root zone of potato and sugar beet using the STICS soil–crop model

The quaternary aquifer of Vitoria-Gasteiz (Basque Country, Northern Spain) is characterised by a shallow water table mainly fed by drainage water, and thus constitutes a vulnerable zone in regards to nitrate pollution. Field studies were performed with a potato crop in 1993 and a sugar beet crop in 2002 to evaluate their impact on nitrate leaching. The overall predictive quality of the STICS soil-crop model was first evaluated using field data and then the model was used to analyze dynamically the impacts of different crop management practices on nitrate leaching. The model was evaluated (i) on soil nitrate concentrations at different depths and (ii) on crop yields. The simulated values proved to be in satisfactory agreement with measured values. Nitrate leaching was more pronounced with the potato crop than with the sugar beet experiment due to i) greater precipitation, ii) lower N uptake of the potato crop due to shallow root depth, and iii) a shorter period of growth. The potato experiment showed that excessive irrigation could significantly increase nitrate leaching by increasing both drainage and nitrate concentrations. The different levels of N-fertilization examined in the sugar beet study had no notable effects on nitrate leaching due to its high N uptake capacity. Complementary virtual experiments were carried out using the STICS model. Our study confirmed that in vulnerable zones agricultural practices must be adjusted, that is to say: 1) N-fertilizer should not be applied in autumn before winter crops; 2) crops with low N uptake capacity (e.g. potatoes) should be avoided or should be preceded and followed by nitrogen catch crops or cover crops; 3) the nitrate concentration of irrigation water should be taken into account in calculation of the N-fertilization rate, and 4) N-fertilization must be precisely adjusted in particular for potato crops.

Intercropping with pulses to concentrate nitrogen and sulphur in wheat

SUMMARY The effects of intercropping wheat with faba bean (Denmark, Germany, Italy and UK) and wheat with pea (France), in additive and replacement designs on grain nitrogen and sulphur concentrations were studied in field experiments in the 2002/03, 2003/04 and 2004/05 growing seasons. Inter- cropping wheat with grain legumes regularly increased the nitrogen concentration of the cereal grain, irrespective of design or location. Sulphur concentration of the cereal was also increased by inter- cropping, but less regularly and to a lesser extent compared with effects on nitrogen concentration. Nitrogen concentration (g/kg) in wheat additively intercropped with faba bean was increased by 8 % across all sites (weighted for inverse of variance), but sulphur concentration was only increased by 4 %, so N :S ratio was also increased by 4 %. Intercropping wheat with grain legumes increased sodium dodecyl sulphate (SDS)-sedimentation volume. The effect of intercropping on wheat nitrogen concentration was greatest when intercropping had the most deleterious effect on wheat yield and the least deleterious effect on pulse yield. Over all sites and seasons, and irrespective of whether the design was additive or replacement, increases in crude protein concentration in the wheat of 10 g/kg by intercropping with faba bean were associated with 25-30 % yield reduction of the wheat, compared with sole-cropped wheat. It was concluded that the increase in protein concentration of wheat grain in intercrops could be of economic benefit when selling wheat for breadmaking, but only if the bean crop was also marketed effectively.

A package of parameter estimation methods and implementation for the STICS crop-soil model

Parameter estimation for complex process models used in agronomy or the environmental sciences is important, because it is a major determinant of model predictive power, and difficult, because the models and associated data are complex. Statistics provides guidance for parameter estimation under various assumptions concerning model error, but it is hard to know which assumptions are most acceptable for these models. We therefore propose a collection of parameter estimation methods. All are based on weighted least squares, but different assumptions lead to different weights. The methods allow one to fit simultaneously several different response variables. One can assume that all errors are independent or on the contrary are correlated. One can assume that model error has expectation zero or not. A software package called OptimiSTICS has been developed, that allows one to implement all of the proposed methods with the STICS crop-soil model. The software can in addition treat the case where some parameters are genotype specific while others are common to all genotypes. The software can also automatically do several sequential stages of parameter estimation. An example is presented, which shows the information that can be obtained, and the conclusions drawn, from comparing the different estimation methods.

Spatially explicit estimates of N2O emissions from croplands suggest climate mitigation opportunities from improved fertilizer management

With increasing nitrogen (N) application to croplands required to support growing food demand, mitigating N2 O emissions from agricultural soils is a global challenge. National greenhouse gas emissions accounting typically estimates N2 O emissions at the country scale by aggregating all crops, under the assumption that N2 O emissions are linearly related to N application. However, field studies and meta-analyses indicate a nonlinear relationship, in which N2 O emissions are relatively greater at higher N application rates. Here, we apply a super-linear emissions response model to crop-specific, spatially explicit synthetic N fertilizer and manure N inputs to provide subnational accounting of global N2 O emissions from croplands. We estimate 0.66 Tg of N2 O-N direct global emissions circa 2000, with 50% of emissions concentrated in 13% of harvested area. Compared to estimates from the IPCC Tier 1 linear model, our updated N2 O emissions range from 20% to 40% lower throughout sub-Saharan Africa and Eastern Europe, to >120% greater in some Western European countries. At low N application rates, the weak nonlinear response of N2 O emissions suggests that relatively large increases in N fertilizer application would generate relatively small increases in N2 O emissions. As aggregated fertilizer data generate underestimation bias in nonlinear models, high-resolution N application data are critical to support accurate N2 O emissions estimates.

Carbohydrate remobilization from storage root to leaves after a stress release in sugar beet ( Beta vulgaris L.): experimental and modelling approaches

Carbohydrate remobilization from the sugar beet storage root to support leaf regrowth after release from water stress was demonstrated by experimental and modelling approaches. Experimental trials were carried out in northern France in 1994 and 1995 and in southern France in 2005, in conditions that involved a succession of soil moisture stresses and re-hydrations. Drought stress slowed leaf growth and the subsequent release of stress resulted in regrowth. A second trial showed that after total defoliation, sugar beet was able to produce new leaves. It was assumed that this leaf renewal, observed at drought stress release or after defoliation, relied on the possibility of remobilizing carbohydrates from storage roots to above-ground organs. This assumption was tested through a heuristic modelling approach, involving the STICS crop model and its existing sub-model on remobilization. The relevance of these formalizations for sugar beet was tested on the experimental data to validate the plant behaviour concerning remobilization. The model succeeded in reproducing leaf area index (LAI) dynamic trends and particularly leaf re-growth after drought stress release or defoliation, despite an over-estimation of the drought stress effect involving an inaccurate simulation of the changes in LAI. Nevertheless, the models ability to forecast accurately above-ground and storage root dry weight, as well as trends in LAI dynamics, showed that the assumptions made about remobilization were able to explain sugar beet behaviour.

Pesticide fate modeling in soils with the crop model STICS: Feasibility for assessment of agricultural practices.

Numerous pesticide fate models are available, but few of them are able to take into account specific agricultural practices, such as catch crop, mixing crops or tillage in their predictions. In order to better integrate crop management and crop growth in the simulation of diffuse agricultural pollutions, and to manage both pesticide and nitrogen pollution, a pesticide fate module was implemented in the crop model STICS. The objectives of the study were: (i) to implement a pesticide fate module in the crop model STICS; (ii) to evaluate the model performance using experimental data from three sites with different pedoclimatic contexts, one in The Netherlands and two in northern France; (iii) to compare the simulations with several pesticide fate models; and (iv) to test the impact of specific agricultural practices on the transfer of the dissolved fraction of pesticides. The evaluations were carried out with three herbicides: bentazone, isoproturon, and atrazine. The strategy applied in this study relies on a noncalibration approach and sensitivity test to assess the operating limits of the model. To this end, the evaluation was performed with default values found in the literature and completed by sensitivity tests. The extended version of the STICS named STICS-Pest, shows similar results with other pesticide fate models widely used in the literature. Moreover, STICS-Pest was able to estimate realistic crop growth and catch crop dynamic, which thus illustrate agricultural practices leading to a reduction of nitrate and a change in pesticide leaching. The dynamic plot-scale model, STICS-Pest is able to simulate nitrogen and pesticide fluxes, when the hydrologic context is in the validity range of the reservoir (or capacity) model. According to these initial results, the model may be a relevant tool for studying the effect of long-term agricultural practices on pesticide residue dynamics in soil and the associated diffuse pollution transfer.

IPSIM-Web, An Online Resource for Promoting Qualitative Aggregative Hierarchical Network Models to Predict Plant Disease Risk: Application to Brown Rust on Wheat

A qualitative pest modeling platform, named Injury Profile Simulator (IPSIM), provides a tool to design aggregative hierarchical network models to predict the risk of pest injuries, including diseases, on a given crop based on variables related to cropping practices as well as soil and weather environment at the field level. The IPSIM platform enables modelers to combine data from various sources (literature, survey, experiments, and so on), expert knowledge, and simulation to build a network-based model. The overall structure of the platform is fully described at the IPSIM-Web website ( www6.inra.fr/ipsim ). A new module called IPSIM-Wheat-brown rust is reported in this article as an example of how to use the system to build and test the predictive quality of a prediction model. Model performance was evaluated for a dataset comprising 1,788 disease observations at 13 French cereal-growing regions over 15 years. Accuracy of the predictions was 85% and the agreement with actual values was 0.66 based on Cohens κ. The new model provides risk information for farmers and agronomists to make scientifically sound tactical (within-season) decisions. In addition, the model may be of use for ex post diagnoses of diseases in commercial fields. The limitations of the model such as low precision and threshold effects as well as the benefits, including the integration of different sources of information, transparency, flexibility, and a user-friendly interface, are discussed.