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Agroecology
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Frida Keuper
France
INRAE ACBB-GC
Research Institute
arable cropping systems, long-term experiment, SOC stocks, C & N cycles
- at the farm level and its immediate surroundings
We are an INRAE research site monitoring C, N and water cycles in 8 different arable cropping systems in Northern France. We are interested in joining a consortium with our existing data, including soil organic carbon and nitrogen stocks 2009-2021, as well as detailed data on plant biomass, N20 (automatic chambers) and CO2 (flux tower) measurements. Additionally, we can include an upcoming SOC stock measurement campaign (2028) in the proposal, if suitable.
The INRAE ACBB long-term experimental research site on arable cropping systems was established in 2010 in Estrées-Mons, Northern France (49°52'25.7"N 3°01'54.1"E, 22 ha). We monitor environmental impacts and performance of arable cropping systems relevant to regional agriculture and to increase understanding of agroecological processes.
Approach:
Six-yearly crop rotations are implemented for all eight treatments (n=4, 32 plots).
Five specific treatment comparisons allow for in-depth analysis of the effects of the following factors:
1) tillage type (20 cm depth vs. 7 cm depth);
2) residue management (restitution/export);
3) N fertilization (100% and 35% of reference treatment);
4) perennial (biomass) crop frequency;
5) legume frequency (low or high frequency of legumes in the rotation, as main crops or cover crops).
Three treatments resemble a system approach, i.e. multiple factors altered simulteneously, in which inputs (mineral nitrogen, pesticides) are reduced, or eliminated and associated management practices (tillage, mechanical weeding) are adapted.
Measurements & Data Storage:
Key variables to assess changes in production, losses to the environment (i.e. N2O, CO2, NO3, pesticides) and carbon storage in the soil are monitored both manually (i.e. yield, plant growth characteristics, soil and water chemistry, soil biodiversity) and with > 600 permanent sensors for continuous data acquisition (weather data, soil moisture, gas exchange). Plant (dry powder), water (frozen) and soil (dry and freeze-dried) samples are archived and available for reference.
Legacy data:
Collected data are stored in an online database (AIDA: PostgreSQL relational database and web interface) managed by INRAE 1158 UMR BioEcoAgro. Raw data are digitized and can be extracted by queries fit to needs (upon request). Queries provide data sets with a "row" format, i.e., for example, "plot", "date", "soil layer" etc. , a "variable" column, a "value" column and a "unit" column, translatable to a columnar format using R.
Approach:
Six-yearly crop rotations are implemented for all eight treatments (n=4, 32 plots).
Five specific treatment comparisons allow for in-depth analysis of the effects of the following factors:
1) tillage type (20 cm depth vs. 7 cm depth);
2) residue management (restitution/export);
3) N fertilization (100% and 35% of reference treatment);
4) perennial (biomass) crop frequency;
5) legume frequency (low or high frequency of legumes in the rotation, as main crops or cover crops).
Three treatments resemble a system approach, i.e. multiple factors altered simulteneously, in which inputs (mineral nitrogen, pesticides) are reduced, or eliminated and associated management practices (tillage, mechanical weeding) are adapted.
Measurements & Data Storage:
Key variables to assess changes in production, losses to the environment (i.e. N2O, CO2, NO3, pesticides) and carbon storage in the soil are monitored both manually (i.e. yield, plant growth characteristics, soil and water chemistry, soil biodiversity) and with > 600 permanent sensors for continuous data acquisition (weather data, soil moisture, gas exchange). Plant (dry powder), water (frozen) and soil (dry and freeze-dried) samples are archived and available for reference.
Legacy data:
Collected data are stored in an online database (AIDA: PostgreSQL relational database and web interface) managed by INRAE 1158 UMR BioEcoAgro. Raw data are digitized and can be extracted by queries fit to needs (upon request). Queries provide data sets with a "row" format, i.e., for example, "plot", "date", "soil layer" etc. , a "variable" column, a "value" column and a "unit" column, translatable to a columnar format using R.