Benjamin Dumont

Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone

Agronomic input and management practices have traditionally been applied uniformly on agricultural fields despite the presence of spatial variability of soil properties and landscape position. When spatial variability is ignored, uniform agronomic management can be both economically and environmentally inefficient. The objectives of this study were to: i) identify optimal N fertilizer rates using an integrated spatio-temporal analysis of yield and site-specific N rate response; ii) test the sensitivity of site specific N management to nitrate leaching in response to different N rates; and iii) demonstrate the environmental benefits of variable rate N fertilizer in a Nitrate Vulnerable Zone. This study was carried out on a 13.6 ha field near the Venice Lagoon, northeast Italy over four years (2005-2008). We utilized a validated crop simulation model to evaluate crop response to different N rates at specific zones in the field based on localized soil and landscape properties under rainfed conditions. The simulated rates were: 50 kg N ha(-1) applied at sowing for the entire study area and increasing fractions, ranging from 150 to 350 kg N ha(-1) applied at V6 stage. Based on the analysis of yield maps from previous harvests and soil electrical resistivity data, three management zones were defined. Two N rates were applied in each of these zones, one suggested by our simulation analysis and the other with uniform N fertilization as normally applied by the producer. N leaching was lower and net revenue was higher in the zones where variable rates of N were applied when compared to uniform N fertilization. This demonstrates the efficacy of using crop models to determine variable rates of N fertilization within a field and the application of variable rate N fertilizer to achieve higher profit and reduce nitrate leaching.

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)...

Evaluating the impact of soil conservation measures on soil organic carbon at the farm scale

The study compares the CO2 emission and sequestration patterns of agricultural soils.Field measurements were used to calibrate first and then validate the SALUS model.Simulations indicated that SOC oxidation rates were substantially lower under No-Tillage.This highlights the benefits of NT adoption in terms of fertility and CO2 mitigation. No-tillage (NT) is considered the least invasive conservation agriculture technique and has shown to be the effective in increasing soil C stocks, and reducing losses compared to others tillage systems. In Italy, the Veneto Region was the first to establish a subsidies scheme aimed at promoting the adoption of NT practices. This program encourages farmers to perform direct seeding, alternate autumn and winter crops and maintain soil cover throughout the year by leaving crop residues or sowing cover crops.The goals of this study were to: (i) compare the CO2 emission and soil C sequestration patterns of agricultural soils under CT and NT management practices in the Veneto region and (ii) analyse the potential mid-term benefits (20102025) of NT management in terms of soil organic C dynamics and CO2 balance. Agronomic data and soil organic carbon levels were measured from 2010 to 2014 in eight farms in the Veneto region that had adopted CT and NT techniques. Field measurements were used to calibrate first and then validate the SALUS model to compare the mid-term impact of CT and NT practices using climate projections. SOC carbon pools in the model were initialized using the procedure described in Basso et al. (2011c). This is the first study to employ a model using such an extensive dataset at the farm level to assess the CT and NT strategies within this region.Results of this research will assist farmers and policy makers in the region to define the tillage systems most suited to improve soil C stocks and thereby minimize CO2 emissions from agricultural soils. Overall, simulations indicated that SOC stocks can decrease under both CT and NT regimes, however SOC oxidation rates were substantially lower under NT. Critically, the greatest reduction in CO2 emission was observed when NT was adopted in soil with high levels of SOM. This highlights the benefits of NT adoption in terms of soil fertility preservation and CO2 emissions mitigation.

Tradeoffs between Maize Silage Yield and Nitrate Leaching in a Mediterranean Nitrate- Vulnerable Zone under Current and Projected Climate Scenarios

Future climatic changes may have profound impacts on cropping systems and affect the agronomic and environmental sustainability of current N management practices. The objectives of this work were to i) evaluate the ability of the SALUS crop model to reproduce experimental crop yield and soil nitrate dynamics results under different N fertilizer treatments in a farmer’s field, ii) use the SALUS model to estimate the impacts of different N fertilizer treatments on NO3- leaching under future climate scenarios generated by twenty nine different global circulation models, and iii) identify the management system that best minimizes NO3- leaching and maximizes yield under projected future climate conditions. A field experiment (maize-triticale rotation) was conducted in a nitrate vulnerable zone on the west coast of Sardinia, Italy to evaluate N management strategies that include urea fertilization (NMIN), conventional fertilization with dairy slurry and urea (CONV), and no fertilization (N0). An ensemble of 29 global circulation models (GCM) was used to simulate different climate scenarios for two Representative Circulation Pathways (RCP6.0 and RCP8.5) and evaluate potential nitrate leaching and biomass production in this region over the next 50 years. Data collected from two growing seasons showed that the SALUS model adequately simulated both nitrate leaching and crop yield, with a relative error that ranged between 0.4% and 13%. Nitrate losses under RCP8.5 were lower than under RCP6.0 only for NMIN. Accordingly, levels of plant N uptake, N use efficiency and biomass production were higher under RCP8.5 than RCP6.0. Simulations under both RCP scenarios indicated that the NMIN treatment demonstrated both the highest biomass production and NO3- losses. The newly proposed best management practice (BMP), developed from crop N uptake data, was identified as the optimal N fertilizer management practice since it minimized NO3- leaching and maximized biomass production over the long term.

Assessing and modeling economic and environmental impact of wheat nitrogen management in Belgium

Future progress in wheat yield will rely on identifying genotypes and management practices better adapted to the fluctuating environment. Nitrogen (N) fertilization is probably the most important practice impacting crop growth. However, the adverse environmental impacts of inappropriate N management (e.g., lixiviation) must be considered in the decision-making process. A formal decisional algorithm was developed to tactically optimize the economic and environmental N fertilization in wheat. Climatic uncertainty analysis was performed using stochastic weather time-series (LARS-WG). Crop growth was simulated using STICS model. Experiments were conducted to support the algorithm recommendations: winter wheat was sown between 2008 and 2014 in a classic loamy soil of the Hesbaye Region, Belgium (temperate climate). Results indicated that, most of the time, the third N fertilization applied at flag-leaf stage by farmers could be reduced. Environmental decision criterion is most of the time the limiting factor in comparison to the revenues expected by farmers. The economic and environmental impact of Nitrogen fertilization was evaluated.A complete and generic methodology for tactical N optimization is proposed.Climatic conditions occurring between sowing and flag leaf stage greatly impacts N optimization.Environment?× management interactions have to be considered when optimizing N.Environmental consideration is a more limiting factor than expected revenues for N optimization.

Yield variability linked to climate uncertainty and nitrogen fertilisation

At the parcel scale, crop models such as STICS are powerful tools to study the effects of variable inputs such as management practices (e.g. nitrogen (N) fertilisation). In combination with a weather generator, we built up a general methodology that allows studying the yield variability linked to climate uncertainty, in order to assess the best N practice. Our study highlighted that, applying the Belgian farmer current N practice (60-60-60 kg N/ha), the yield distribution was found to be very asymmetric with a skewness of -1.02 and a difference of 5% between the mean (10.5 t/ha) and the median (11.05 t/ha) of the distribution. This implies that, under such practice, the probability for farmers to achieve decent yields, in comparison to the mean of the distribution, was the highest.

Multimodel ensembles improve predictions of crop–environment–management interactions

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions

Nature Plants3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

Assessment of plant leaf area measurement by using stereo-vision

The aim of this study is to develop an alternative measurement for the leaf area index (LAI), an important agronomic parameter for plant growth assessment. A 3D stereo-vision technique was developed to measure both leaf area and corresponding ground area. The leaf area was based on pixel related measurements while the ground area was based on the mean distance from the leaves to the camera. Laboratory and field experiments were undertaken to estimate the accuracy and the precision of the technique. Result showed that, though the leaves-camera distance had to be estimated precisely in order to have accurate measurement, the precision of the LAI evaluation, after regression, was equivalent to the reference measurements, that is to say around 10% of the estimated value. This shows the potential of the 3D measurements compared with tedious reference measurements.

Prediction of non-linear time-variant dynamic crop model using Bayesian methods

This work addresses the problem of predicting a non-linear time-variant leaf area index and soil moisture model (LSM) using state estimation. These techniques include the extended Kalman filter (EKF), particle filter (PF) and the more recently developed technique, variational filter (VF). In the comparative study, the state variables (the leaf-area index LAI, the volumetric water content of the layer 1, HUR1 and the volumetric water content of the layer 2, HUR2) are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square error with respect to the noise-free data. The results show that VF provides a significant improvement over EKF and PF.