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

Dumont, Benjamin; Basso, Bruno; Bodson, Bernard; Destain, Jean‐Pierre; Destain, Marie‐France

doi:10.1016/j.envsoft.2016.02.015

Cited by 24 publications

(9 citation statements)

References 44 publications

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“…Soil N mineralization from SOC and crop N uptake, and N losses are three important components defining the optimum N rate, however, these processes are dynamic and difficult to predict (Cassman et al, 2002). Therefore N management tools that simultaneously consider dynamics in soil organic carbon mineralization, crop growth, weather conditions, and agronomic practices may greatly improve site- and year-specific EONR estimates (Basso et al, 2012, 2016; Dumont et al, 2016). Dynamic cropping system simulation models such as Agricultural Production Systems sIMulator (APSIM; Holzworth et al, 2014), DSSAT (Jones et al, 2003), RZWQM (Ahuja et al, 2000), CropSyst (Stockle et al, 2003), SALUS (Basso et al, 2006), and others have been used to investigate soil-crop-weather dynamics, however, model use has been limited to address long-term optimum N rates (Ma et al, 2007; Basso et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

et al. 2016

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Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR’s were within the historical N rate error range (40–50 kg N ha-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.

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Section: Introductionmentioning

confidence: 99%

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

et al. 2016

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“…By utilizing a remote sensing approach to make N management decisions, we integrated three important and dynamic components which define the optimum N rate: soil N mineralization from OM, crop N uptake, and N losses [78,98]. The integration of multiple layers of information representing the complexity of the cropping system may greatly improve the site-and year-specific EONR estimates [99][100][101][102].…”

Section: Implications and Limitations Of The Uav Sensor-based N Recommentioning

confidence: 99%

Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn

Thompson

Puntel

2020

Remote Sensing

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Determining the optimal nitrogen (N) rate in corn remains a critical issue, mainly due to unaccounted spatial (e.g., soil properties) and temporal (e.g., weather) variability. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors may provide opportunities to improve N management by the timely informing of spatially variable, in-season N applications. Here, we developed a practical decision support system (DSS) to translate spatial field characteristics and normalized difference red edge (NDRE) values into an in-season N application recommendation. On-farm strip-trials were established at three sites over two years to compare farmer’s traditional N management to a split-application N management guided by our UAV sensor-based DSS. The proposed systems increased nitrogen use efficiency 18.3 ± 6.1 kg grain kg N−1 by reducing N rates by 31 ± 6.3 kg N ha−1 with no yield differences compared to the farmers’ traditional management. We identify five avenues for further improvement of the proposed DSS: definition of the initial base N rate, estimation of inputs for sensor algorithms, management zone delineation, high-resolution image normalization approach, and the threshold for triggering N application. Two virtual reference (VR) methods were compared with the high N (HN) reference strip method for normalizing high-resolution sensor data. The VR methods resulted in significantly lower sufficiency index values than those generated by the HN reference, resulting in N fertilization recommendations that were 31.4 ± 10.3 kg ha−1 higher than the HN reference N fertilization recommendation. The use of small HN reference blocks in contrasting management zones may be more appropriate to translate field-scale, high-resolution imagery into in-season N recommendations. In view of a growing interest in using UAVs in commercial fields and the need to improve crop NUE, further work is needed to refine approaches for translating imagery into in-season N recommendations.

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“…1). This ecozone is the major corn production ecozone in Canada and extends from southern Ontario to southwestern Quebec ( (Brown and Bootsma 1993). Cumulative CHU is calculated by summation of daily values from May 1st to the date when temperature drops below − 2°C for the first time.…”

Section: Climate Variation In the Mixedwood Plains Ecozonementioning

confidence: 99%

New model-based insights for strategic nitrogen recommendations adapted to given soil and climate

Mesbah

Pattey

Jégo

et al. 2018

Agron. Sustain. Dev.

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Managing nitrogen (N) fertilizer applied in agricultural fields is important for increasing crop productivity while limiting the environmental contamination caused by release of reactive N, especially for crops with high N demand (e.g., corn, Zea mays L.). However, for given soil properties, the optimum amount of N applied depends on climatic conditions. The central question to N management is then what should be the recommended N rate for given soil and climate that would minimize the release of reactive N while maintaining the crop productivity. To address this central challenge of N management, we used a recently developed model-based methodology (called "Identifying NEMO"), which was proved to be effective in identifying ecophysiological optimum N rate and optimum nitrogen use efficiency (NUE opt ). We performed modeling for dominant soils and various agroclimatic conditions in five regions along the Mixedwood Plains ecozone, where more than 90% of Canadian corn production takes place. Here, we analyzed for the first time the effect of soil and climate on ecophysiological optimum N rate in an ecozone where there exists a significant agroclimatic gradient. Our results indicated that there were some commonalities among all soils and regions, which we could classify them into two groups with NUE opt ranging from 10 to 17 kg dry yield kg −1 N. For cases with low NUE opt , the recommended N for an expected dry yield of 8 t ha −1 varied from 115 to 199 kg ha −1 , whereas they were much lower (79-154 kg ha −1 ) for cases with high NUE opt . These recommendations were 20-40 kg ha −1 lower than provincial recommendations. Moreover, we found that the different behavior of the two groups was due to soil textures and soils available water holding capacity. For most locations, soils with intermediate available water holding capacity (i.e., 12-15%v) had relatively higher expected yield and lower recommended N.

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

Cited by 24 publications

References 44 publications

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn

New model-based insights for strategic nitrogen recommendations adapted to given soil and climate

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