Management and spatial resolution effects on yield and water balance at regional scale in crop models

Constantin, Julie; Raynal, Hélène; Casellas, Éric; Hoffmann, Holger; Bindi, Marco; Doro, Luca; Eckersten, Henrik; Gaiser, Thomas; Grosz, Balasz; Haas, Edwin; Kersebaum, Kurt Christian; Klatt, Steffen; Kuhnert, Matthias; Lewan, Elisabet; Maharjan, Ganga Ram; Moriondo, Marco; Nendel, Claas; Roggero, Pier Paolo; Specka, Xenia; Trombi, Giacomo; Villa, Ana; Wang, Enli; Weihermüller, Lutz; Yeluripati, Jagadeesh; Zhao, Zhigan; Ewert, Frank; Bergez, Jacques-Éric

doi:10.1016/j.agrformet.2019.05.013

Cited by 32 publications

(11 citation statements)

References 52 publications

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“…Each simulation unit has characteristic soil properties, agricultural management and daily meteorological data. The 0.25 • grid has been identified to attain an adequate distribution of the spatial variability in the input data, to attain representativeness of local effects on a European scale and to limit computational burdens (Hoffmann et al, 2016;Constantin et al, 2019). Two distinct periods of temporal aggregation have been considered.…”

Section: Methodsmentioning

confidence: 99%

“…More recently, process-based models conceived for site-scale representation have been applied at the regional scales to, for example, calculate national GHG inventories (Smith, 2013) or build statistical models (Del Grosso et al, 2009;Haas et al, 2013). The main challenges to carrying out spatial assessments are represented by the availability and resolution of the input data (Lugato et al, 2014(Lugato et al, , 2017, by the biases introduced into the aggregation or disaggregation of these data in homogeneous spatial areas (Constantin et al, 2019;Hansen and Jones, 2000), and by the model validity regarding spatial-scale change (Hoffmann et al, 2016). Furthermore, the simulation of agricultural production with climate projections introduces an additional degree of uncertainty that can be reduced with a sound evaluation of historical data (Rosenzweig et al, 2013), as proposed in this study.…”

Section: Introductionmentioning

confidence: 99%

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Effects of climate change in European croplands and grasslands: productivity, greenhouse gas balance and soil carbon storage

et al. 2022

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Abstract. Knowledge of the effects of climate change on agro-ecosystems is fundamental to identifying local actions aimed to maintain productivity and reduce environmental issues. This study investigates the effects of climate perturbation on the European crop and grassland production systems, combining the findings from two specific biogeochemical models. Accurate and high-resolution management and pedoclimatic data were employed. Results have been verified for the period 1978–2004 (historical period) and projected until 2099 with two divergent intensities: the Intergovernmental Panel on Climate Change (IPCC) climate projections, Representative Concentration Pathway (RCP) 4.5 and RCP8.5. We have provided a detailed overview of productivity and the impacts on management (sowing dates, water demand, nitrogen use efficiency). Biogenic greenhouse gas balance (N2O, CH4, CO2) was calculated, including an assessment of the gases' sensitivity to the leading drivers, and a net carbon budget on production systems was compiled. Results confirmed a rise in productivity in the first half of the century (+5 % for croplands at +0.2 t DM ha−1 yr−1, +1 % for grasslands at +0.1 t DM ha−1 yr−1; DM denotes dry matter), whereas a significant reduction in productivity is expected during the period 2050–2099, caused by the shortening of the length of the plant growing cycle associated with rising temperatures. This effect was more pronounced for the more pessimistic climate scenario (−6.1 % for croplands and −7.7 % for grasslands), for the Mediterranean regions and in central European latitudes, confirming a regionally distributed impact of climate change. Non-CO2 greenhouse gas emissions were triggered by rising air temperatures and increased exponentially over the century, often exceeding the CO2 accumulation of the explored agro-ecosystems, which acted as potential C sinks. The emission factor for N2O was 1.82 ± 0.07 % during the historical period and rose to up to 2.05 ± 0.11 % for both climate projections. The biomass removal (crop yield, residues exports, mowing and animal intake) converted croplands and grasslands into net C sources (236 ± 107 Tg CO2 eq. yr−1 in the historical period), increasing from 19 % to 26 % during the climate projections, especially for RCP4.5. Nonetheless, crop residue restitution might represent a potential management strategy to overturn the C balance. Although with a marked latitudinal gradient, water demand will double over the next few decades in the European croplands, whereas the benefit in terms of yield (+2 % to +10 % over the century) will not contribute substantially to balance the C losses due to climate perturbation.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of climate change in European croplands and grasslands: productivity, greenhouse gas balance and soil carbon storage

et al. 2022

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“…When models have been calibrated and evaluated, their outputs can be used instead of real observations, thus model spatialization may be desirable to reduce the working time and cost of obtaining measurements in the field (Acevedo-Opazo et al, 2008Baralon et al, 2012). Models can be used to obtain difficult, infeasible or unavailable measurements (Constantin et al, 2019).…”

Section: Complete Data Setsmentioning

confidence: 99%

A review of methods to evaluate crop model performance at multiple and changing spatial scales

et al. 2022

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Crop models are useful tools because they can help understand many complex processes by simulating them. They are mainly designed at a specific spatial scale, the field. But with the new spatial data being made available in modern agriculture, they are being more and more applied at multiple and changing scales. These applications range from typically at broader scales, to perform regional or national studies, or at finer scales to develop modern site-specific management approaches. These new approaches to the application of crop models raise new questions concerning the evaluation of their performance, particularly for downscaled applications. This article first reviews the reasons why practitioners decide to spatialize crop models and the main methods they have used to do this, which questions the best place of the spatialization process in the modelling framework. A strong focus is then given to the evaluation of these spatialized crop models. Evaluation metrics, including the consideration of dedicated sensitivity indices are reviewed from the published studies. Using a simple example of a spatialized crop model being used to define management zones in precision viticulture, it is shown that classical model evaluation involving aspatial indices (e.g. the RMSE) is not sufficient to characterize the model performance in this context. A focus is made at the end of the review on potentialities that a complementary evaluation could bring in a precision agriculture context.

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“…crop rotations, tillage, irrigation, fertilization) and climate change on crop production and environmental quality (e.g. Bergez et al., 2014; Brilli et al., 2017; Constantin et al., 2019; Dilla, Smethurst, Barry, Parsons, & Denboba, 2018; Eckersten et al., 2012; Robertson, Rebetzke, & Norton, 2015). Existing models can be used to quantify the effects of soil degradation by running scenario simulations with contrasting soil physical and hydraulic properties.…”

Section: Introductionmentioning

confidence: 99%

A framework for modelling soil structure dynamics induced by biological activity

Meurer

Barron

Chenu

et al. 2020

Global Change Biology

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106

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Soil degradation is a worsening global phenomenon driven by socio‐economic pressures, poor land management practices and climate change. A deterioration of soil structure at timescales ranging from seconds to centuries is implicated in most forms of soil degradation including the depletion of nutrients and organic matter, erosion and compaction. New soil–crop models that could account for soil structure dynamics at decadal to centennial timescales would provide insights into the relative importance of the various underlying physical (e.g. tillage, traffic compaction, swell/shrink and freeze/thaw) and biological (e.g. plant root growth, soil microbial and faunal activity) mechanisms, their impacts on soil hydrological processes and plant growth, as well as the relevant timescales of soil degradation and recovery. However, the development of such a model remains a challenge due to the enormous complexity of the interactions in the soil–plant system. In this paper, we focus on the impacts of biological processes on soil structure dynamics, especially the growth of plant roots and the activity of soil fauna and microorganisms. We first define what we mean by soil structure and then review current understanding of how these biological agents impact soil structure. We then develop a new framework for modelling soil structure dynamics, which is designed to be compatible with soil–crop models that operate at the soil profile scale and for long temporal scales (i.e. decades, centuries). We illustrate the modelling concept with a case study on the role of root growth and earthworm bioturbation in restoring the structure of a severely compacted soil.

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Management and spatial resolution effects on yield and water balance at regional scale in crop models

Cited by 32 publications

References 52 publications

Effects of climate change in European croplands and grasslands: productivity, greenhouse gas balance and soil carbon storage

Effects of climate change in European croplands and grasslands: productivity, greenhouse gas balance and soil carbon storage

A review of methods to evaluate crop model performance at multiple and changing spatial scales

A framework for modelling soil structure dynamics induced by biological activity

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