International audience | This article presents data designed by European researchers who performed a literature review and interpreted the results to determine impact factors of many agroecological practices on a wide variety of sustainability indicators. The impact factors are represented in a matrix that connects practices to indicators. The indicators are related to environmental, economic and social sustainability of a typical European integrated crop-livestock farm. The data are included in the serious game SEGAE to learn agroecology, as described in “SEGAE: a serious game to learn agroecology” [1]. The data can be modified to adapt the game to other agricultural systems. Finally, the data can be re-used in research projects as a basis to assess impacts of agroecological practices.

peer reviewed | This article presents data designed by European researchers who performed a literature review and interpreted the results to determine impact factors of many agroecological practices on a wide variety of sustainability indicators. The impact factors are represented in a matrix that connects practices to indicators. The indicators are related to environmental, economic and social sustainability of a typical European integrated crop-livestock farm. The data are included in the serious game SEGAE to learn agroecology, as described in “SEGAE: a serious game to learn agroecology” [1]. The data can be modified to adapt the game to other agricultural systems. Finally, the data can be re-used in research projects as a basis to assess impacts of agroecological practices.

This article presents data designed by European researchers who performed a literature review and interpreted the results to determine impact factors of many agroecological practices on a wide variety of sustainability indicators. The impact factors are represented in a matrix that connects practices to indicators. The indicators are related to environmental, economic and social sustainability of a typical European integrated crop-livestock farm. The data are included in the serious game SEGAE to learn agroecology, as described in “SEGAE: a serious game to learn agroecology” [1]. The data can be modified to adapt the game to other agricultural systems. Finally, the data can be re-used in research projects as a basis to assess impacts of agroecological practices.

This article presents data designed by European researchers who performed a literature review and interpreted the results to determine impact factors of many agroecological practices on a wide variety of sustainability indicators. The impact factors are represented in a matrix that connects practices to indicators. The indicators are related to environmental, economic and social sustainability of a typical European integrated crop-livestock farm. The data are included in the serious game SEGAE to learn agroecology, as described in “SEGAE: a serious game to learn agroecology” [1]. The data can be modified to adapt the game to other agricultural systems. Finally, the data can be re-used in research projects as a basis to assess impacts of agroecological practices. | Sustainability indicators, crop-livestock integration, systems approach, transition management | 40 | 1-6 | 36

International audience | Crop monitoring is essential for ensuring food security in a global context of population growth and climate change. Satellite images are commonly used to estimate crop parameters over large areas, and the freely available Synthetic Aperture Radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) images are relevant for that purpose combining high temporal resolution and high spatial resolution. For this data article, field surveys were conducted from January to July 2017 in France to sample wheat and rapeseed crop parameters during the entire crops cycle. Phenological stages were identified in 83 wheat fields and 32 rapeseed fields in Brittany and Picardy regions. Moreover, Leaf Area Index (LAI), wet biomass, dry biomass and water content were sampled in three wheat fields and three rapeseed fields in Brittany. We assigned to each field sample 10 spectral bands and 12 vegetation indices from S-2 images and two backscattering coefficients, one backscattering ratio and four polarimetric indicators from S-1 images. This dataset can be used for crop monitoring in other regions, as well as for modelling development.

Crop monitoring is essential for ensuring food security in a global context of population growth and climate change. Satellite images are commonly used to estimate crop parameters over large areas, and the freely available Synthetic Aperture Radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) images are relevant for that purpose combining high temporal resolution and high spatial resolution. For this data article, field surveys were conducted from January to July 2017 in France to sample wheat and rapeseed crop parameters during the entire crops cycle. Phenological stages were identified in 83 wheat fields and 32 rapeseed fields in Brittany and Picardy regions. Moreover, Leaf Area Index (LAI), wet biomass, dry biomass and water content were sampled in three wheat fields and three rapeseed fields in Brittany. We assigned to each field sample 10 spectral bands and 12 vegetation indices from S-2 images and two backscattering coefficients, one backscattering ratio and four polarimetric indicators from S-1 images. This dataset can be used for crop monitoring in other regions, as well as for modelling development.

Crop monitoring is essential for ensuring food security in a global context of population growth and climate change. Satellite images are commonly used to estimate crop pa- rameters over large areas, and the freely available Synthetic Aperture Radar (SAR) Sentinel-1 (S-1) and optical Sentinel- 2 (S-2) images are relevant for that purpose combining high temporal resolution and high spatial resolution. For this data article, field surveys were conducted from January to July 2017 in France to sample wheat and rapeseed crop pa- rameters during the entire crops cycle. Phenological stages were identified in 83 wheat fields and 32 rapeseed fields in Brittany and Picardy regions. Moreover, Leaf Area Index (LAI), wet biomass, dry biomass and water content were sampled in three wheat fields and three rapeseed fields in Brittany. We assigned to each field sample 10 spectral bands and 12 vegetation indices from S-2 images and two backscattering coefficients, one backscattering ratio and four polarimetric indicators from S-1 images. This dataset can be used for crop monitoring in other regions, as well as for modelling development.

International audience | Improvements in ex situ storage of genetic and reproductive materials offer an alternative for endangered livestock breed conservation. This paper presents a dataset for current ex situ collections and in situ population for 179 Spanish livestock breeds of seven species, cattle, sheep, pig, chicken, goat, horse and donkey. Ex situ data was obtained via survey administered to 18 functioning gene banks in Spain and relates to the reproductive genetic materials (semen doses) of 210 livestock breeds distributed across the gene banks. In situ data combines CENSUS information with linear regression techniques and relates to the geographic distribution of 179 Spanish autochthonous livestock breeds (2009-2018), and in situ population projections and extinction probabilities (2019-2060). We use a decision variable defining an “acceptable level of risk” that allows decision makers to specify tolerable levels of in situ breed endangerment when taking ex situ collection and storage decisions.

Improvements in ex situ storage of genetic and reproductive materials offer an alternative for endangered livestock breed conservation. This paper presents a dataset for current ex situ collections and in situ population for 179 Spanish livestock breeds of seven species, cattle, sheep, pig, chicken, goat, horse and donkey. Ex situ data was obtained via survey administered to 18 functioning gene banks in Spain and relates to the reproductive genetic materials (semen doses) of 210 livestock breeds distributed across the gene banks. In situ data combines CENSUS information with linear regression techniques and relates to the geographic distribution of 179 Spanish autochthonous livestock breeds (2009-2018), and in situ population projections and extinction probabilities (2019-2060). We use a decision variable defining an “acceptable level of risk” that allows decision makers to specify tolerable levels of in situ breed endangerment when taking ex situ collection and storage decisions.

Improvements in ex situ storage of genetic and reproductive materials offer an alternative for endangered livestock breed conservation. This paper presents a dataset for current ex situ collections and in situ population for 179 Spanish livestock breeds of seven species, cattle, sheep, pig, chicken, goat, horse and donkey. Ex situ data was obtained via survey administered to 18 functioning gene banks in Spain and relates to the reproductive genetic materials (semen doses) of 210 livestock breeds distributed across the gene banks. In situ data combines CENSUS information with linear regression techniques and relates to the geographic distribution of 179 Spanish autochthonous livestock breeds (2009-2018), and in situ population projections and extinction probabilities (2019-2060). We use a decision variable defining an “acceptable level of risk” that allows decision makers to specify tolerable levels of in situ breed endangerment when taking ex situ collection and storage decisions.

International audience | Milk microfiltration process plays a key role in the dairy industry. Crossflow microfiltration of skimmed milk using a membrane with 0.1 µm mean pore size is widely used to fractionate the two main groups of dairy proteins: casein micelles (~150 nm) and serum proteins (~2-15 nm). Retentate, containing mainly casein micelles, is generally used to enrich vat milk for cheese making. Permeate, containing serum proteins, lactose and minerals, is usually ultrafiltered in order to produce protein-rich concentrate with a high nutritional value dedicated to specific populations such as infants and seniors. The great interest in these protein fractions explains the increasing number of microfiltration equipments in the dairy industry. This data article contains data associated with milk microfiltration process experiments and properties of the resulting dairy fractions annotated from a collection of scientific documents. These data are stored in INRAE public repository (see Data accessibility in the Specification Table for direct links to data). They have been structured using MILK MICROFILTRATION ontology and are replicated in @Web data warehouse providing additional querying tools

Milk microfiltration process plays a key role in the dairy industry. Crossflow microfiltration of skimmed milk using a membrane with 0.1 µm mean pore size is widely used to fractionate the two main groups of dairy proteins: casein micelles (~150 nm) and serum proteins (~2-15 nm). Retentate, containing mainly casein micelles, is generally used to enrich vat milk for cheese making. Permeate, containing serum proteins, lactose and minerals, is usually ultrafiltered in order to produce protein-rich concentrate with a high nutritional value dedicated to specific populations such as infants and seniors. The great interest in these protein fractions explains the increasing number of microfiltration equipments in the dairy industry. This data article contains data associated with milk microfiltration process experiments and properties of the resulting dairy fractions annotated from a collection of scientific documents. These data are stored in INRAE public repository (see Data accessibility in the Specification Table for direct links to data). They have been structured using MILK MICROFILTRATION ontology and are replicated in @Web data warehouse providing additional querying tools (https://www6.inrae.fr/cati-icat-atweb/).

International audience | This dataset is composed of symbolic and quantitative entities concerning food packaging composition and gas permeability. It was created from 50 scientific articles in English registered in html format from several international journals on the ScienceDirect website. The files were annotated independently by three experts on a WebAnno server. The aim of the annotation task was to recognize all entities related to packaging permeability measures and packaging composition. This annotation task is driven by an Ontological and Terminological Resource (OTR). An annotation guideline was designed in a collective and iterative approach involving the annotators. This dataset can be used to train or evaluate natural language processing (NLP) approaches in experimental fields, such as specialized entity recognition (e.g. terms and variations, units of measure, complex numerical values) or sentence level binary relation (e.g. value to unit, term to acronym).

This dataset is composed of symbolic and quantitative entities concerning food packaging composition and gas permeability. It was created from 50 scientific articles in English registered in html format from several international journals on the ScienceDirect website. The files were annotated independently by three experts on a WebAnno server. The aim of the annotation task was to recognize all entities related to packaging permeability measures and packaging composition. This annotation task is driven by an Ontological and Terminological Resource (OTR). An annotation guideline was designed in a collective and iterative approach involving the annotators. This dataset can be used to train or evaluate natural language processing (NLP) approaches in experimental fields, such as specialized entity recognition (e.g. terms and variations, units of measure, complex numerical values) or sentence level binary relation (e.g. value to unit, term to acronym).

International audience | Combined with multivariate calibration methods, near-infrared (NIR) spectroscopy is a non-destructive, rapid, precise and inexpensive analytical method to predict chemical contents of organic products. Nevertheless, one practical limitation of this approach is that performance of the calibration model may decrease when the data are acquired with different spectrometers. To overcome this limitation, standardization methods exist, such as the piecewise direct standardization (PDS) algorithm.The dataset presented in this article consists of 332 manure samples from poultry and cattle, sampled from farms located in major regions of livestock production in mainland France and Reunion Island. The samples were analysed for seven chemical properties following conventional laboratory methods. NIR spectra were acquired with three spectrometers from fresh homogenized and dried ground samples and then standardized using the PDS algorithm. This important dataset can be used to train and test chemometric models and is of particular interest to NIR spectroscopists and agronomists who assess the agronomic value of animal waste. (C) 2020 The Authors. Published by Elsevier Inc.

Combined with multivariate calibration methods, near-infrared (NIR) spectroscopy is a non-destructive, rapid, precise and inexpensive analytical method to predict chemical contents of organic products. Nevertheless, one practical limitation of this approach is that performance of the calibration model may decrease when the data are acquired with different spectrometers. To overcome this limitation, standardization methods exist, such as the piecewise direct standardization (PDS) algorithm.The dataset presented in this article consists of 332 manure samples from poultry and cattle, sampled from farms located in major regions of livestock production in mainland France and Reunion Island. The samples were analysed for seven chemical properties following conventional laboratory methods. NIR spectra were acquired with three spectrometers from fresh homogenized and dried ground samples and then standardized using the PDS algorithm. This important dataset can be used to train and test chemometric models and is of particular interest to NIR spectroscopists and agronomists who assess the agronomic value of animal waste.

Combined with multivariate calibration methods, near-infrared (NIR) spectroscopy is a non-destructive, rapid, precise and inexpensive analytical method to predict chemical contents of organic products. Nevertheless, one practical limitation of this approach is that performance of the calibration model may decrease when the data are acquired with different spectrometers. To overcome this limitation, standardization methods exist, such as the piecewise direct standardization (PDS) algorithm. The dataset presented in this article consists of 332 manure samples from poultry and cattle, sampled from farms located in major regions of livestock production in mainland France and Reunion Island. The samples were analysed for seven chemical properties following conventional laboratory methods. NIR spectra were acquired with three spectrometers from fresh homogenized and dried ground samples and then standardized using the PDS algorithm. This important dataset can be used to train and test chemometric models and is of particular interest to NIR spectroscopists and agronomists who assess the agronomic value of animal waste.

International audience | This dataset complement a previously published dataset [1] and corresponds to the physico-chemical parameters data series produced during the MESOLAC experimental project [2]. The presented dataset is composed of: 1. In situ profiles (0–3m) of temperature, conductivity, pH, dissolved oxygen (concentration and saturation). 2. In situ measurements of light spectral UV/VIS/IR irradiance (300–950 nm wavelength range) taken at 0, 0.25, 0.5, 1, 1.5, 2 and 2.5m. 3. Laboratory chemical analysis of samples collected at 0 and 2 m (conductivity, pH, total alkalinity, NH4, NO2, NO3, total and particulate nitrogen (Ntot, Npart), PO4, total and particulate phosphorus (Ptot, Ppart), total, organic particulate and total particulate carbon (Ctot, Cpart-org, Cpart-tot), Cl, SO4, SiO2. 4. Laboratory analysis of pigments extracted from samples collected at 0 and 2 m (Chla, Chlc, carotenoids, phaeopigments).The experimental design is the same as in Tran-Khac et al [1]. Briefly, it consisted of nine pelagic mesocosms (about 3000 L, 3m depth) deployed in July 2019 in Lake Geneva near the shore of Thonon les Bains (France) aiming to simulate predicted climate scenarios (i.e. extreme events) and assess the response of planktonic communities, ecosystem functioning and resilience.During the experiment, physical parameters were measured twice a week. At the same time, samples were collected at 0 and 2m of depth for subsequent chemical laboratory analyses. These data are presented in the dataset file, ordered by sampling event (numbered from S1 to S8), treatment (Control-C, High-H and Medium-M) and replicates (1 to 3). For each sampling point the measured parameters are listed in columns, missing data and values below the detection limit are marked as NA (not available).This data set aims to contribute to the understanding of the effect of environmental forcing on lake physico-chemical characteristics (such as temperature, oxygen and nutrient concentration) under simulated intense weather events. To a broader extent, the presented data can be used for a wide variety of applications, including monitoring of a large peri-alpine lake functioning under environmental stress and being included in further meta-analysis to generalise the effect of climate change on large lakes. The two complementary dataset differ in the acquired data and methods, temporal and spatial resolution. They complete each other in terms of physico-chemical characterization of the experimental treatments and together can allow comparison of the two different monitoring strategies (continuous vs punctual) during in situ experimental manipulations.

This dataset complement a previously published dataset [1] and corresponds to the physico-chemical parameters data series produced during the MESOLAC experimental project [2]. The presented dataset is composed of: 1. In situ profiles (0–3m) of temperature, conductivity, pH, dissolved oxygen (concentration and saturation). 2. In situ measurements of light spectral UV/VIS/IR irradiance (300–950 nm wavelength range) taken at 0, 0.25, 0.5, 1, 1.5, 2 and 2.5m. 3. Laboratory chemical analysis of samples collected at 0 and 2 m (conductivity, pH, total alkalinity, NH₄, NO₂, NO₃, total and particulate nitrogen (Ntot, Npart), PO₄, total and particulate phosphorus (Ptot, Ppart), total, organic particulate and total particulate carbon (Ctot, Cpart-org, Cpart-tot), Cl, SO₄, SiO₂. 4. Laboratory analysis of pigments extracted from samples collected at 0 and 2 m (Chla, Chlc, carotenoids, phaeopigments).The experimental design is the same as in Tran-Khac et al [1]. Briefly, it consisted of nine pelagic mesocosms (about 3000 L, 3m depth) deployed in July 2019 in Lake Geneva near the shore of Thonon les Bains (France) aiming to simulate predicted climate scenarios (i.e. extreme events) and assess the response of planktonic communities, ecosystem functioning and resilience.During the experiment, physical parameters were measured twice a week. At the same time, samples were collected at 0 and 2m of depth for subsequent chemical laboratory analyses. These data are presented in the dataset file, ordered by sampling event (numbered from S1 to S8), treatment (Control-C, High-H and Medium-M) and replicates (1 to 3). For each sampling point the measured parameters are listed in columns, missing data and values below the detection limit are marked as NA (not available).This data set aims to contribute to the understanding of the effect of environmental forcing on lake physico-chemical characteristics (such as temperature, oxygen and nutrient concentration) under simulated intense weather events. To a broader extent, the presented data can be used for a wide variety of applications, including monitoring of a large peri-alpine lake functioning under environmental stress and being included in further meta-analysis to generalise the effect of climate change on large lakes. The two complementary dataset differ in the acquired data and methods, temporal and spatial resolution. They complete each other in terms of physico-chemical characterization of the experimental treatments and together can allow comparison of the two different monitoring strategies (continuous vs punctual) during in situ experimental manipulations.

International audience | The general purpose of the primary and secondary data available in this article is to support an integrated assessment of scenarios of crop-livestock integration at the territorial level i.e. of exchanges between arable and livestock farms. The data is a result of a research collaboration between the scientist from INRAE, agricultural advisers from Chamber of Agriculture of Pays de la Loire (CRAPL) and a collective of five arable and two livestock farmers located in the district of Pays de Pouzauges (Vendée department, western France). All participants formed part of the DiverIMPACTS project (https://www.diverimpacts.net/) that aims to achieve the full potential of diversification of cropping systems for improved productivity, delivery of ecosystem services and resource-efficient and sustainable value chains in Europe.

The general purpose of the primary and secondary data available in this article is to support an integrated assessment of scenarios of crop-livestock integration at the territorial level i.e. of exchanges between arable and livestock farms. The data is a result of a research collaboration between the scientist from INRAE, agricultural advisers from Chamber of Agriculture of Pays de la Loire (CRAPL) and a collective of five arable and two livestock farmers located in the district of Pays de Pouzauges (Vendée department, western France). All participants formed part of the DiverIMPACTS project (https://www.diverimpacts.net/) that aims to achieve the full potential of diversification of cropping systems for improved productivity, delivery of ecosystem services and resource-efficient and sustainable value chains in Europe. The first dataset corresponds to the inputs of MAELIA (http://maelia-platform.inra.fr/), a spatial agent-based simulation platform that was used to support an iterative design and assessment of scenarios to redesign cropping systems. The second dataset corresponds to the outputs of MAELIA simulations and the associated indicators at the farm, group and territory level. The data comprise multiple shape and csv files characterizing the edaphic-climatic heterogeneity of the territory and cropping systems, farmers’ crop management rules (IF-THEN rules) and general information about the farms (e.g. crops, agricultural equipment, average crop yields). Data is reported for the baseline situation and three exchange scenarios containing different innovative cropping systems co-designed by scientists, agricultural advisers and the farmers. The data presented here can be found in the Portail Data INRA repository (https://doi.org/10.15454/3ZTCF5) and were used in the research article “Fostering local crop-livestock integration via legume exchanges using an innovative integrated assessment and modelling approach: MAELIA” [1].

International audience | The data were collected from a brown mustard seeds collection of 18 accessions during two years and in three distinct sites of production in France. The 18 accessions of mustard seeds were selected to be representative of genetic, agronomical and technological variabilities. All accessions were produced in the "Bourgogne " area. This article describes agronomical data (PMG, yield), genotyping data, global composition of mustard seeds (lipids, proteins and polysaccharides) and fine composition of the previous macronutrients potentially involved in the technological properties (fatty acids, storage proteins and osidic composition of polysaccharides). Additional data regarding the potential rheological property of each accessions were also reported. These data can be reused by food industries, breeders and geneticists in order to understand pedoclimatic effects (year and location) and the relation between mustard seed composition and the end uses properties (paste mustard quality).

The data were collected from a brown mustard seeds collection of 18 accessions during two years and in three distinct sites of production in France. The 18 accessions of mustard seeds were selected to be representative of genetic, agronomical and technological variabilities. All accessions were produced in the “Bourgogne” area. This article describes agronomical data (PMG, yield), genotyping data, global composition of mustard seeds (lipids, proteins and polysaccharides) and fine composition of the previous macronutrients potentially involved in the technological properties (fatty acids, storage proteins and osidic composition of polysaccharides). Additional data regarding the potential rheological property of each accessions were also reported. These data can be reused by food industries, breeders and geneticists in order to understand pedoclimatic effects (year and location) and the relation between mustard seed composition and the end-uses properties (paste mustard quality).

International audience | With the development of agroecosystem approaches, new cropping systems have to be designed to deliver multiple ecosystem services. In this context, we assessed four innovative cropping systems, designed to reach multiple environmental and production goals, in a long-term field experiment (2009-2020) at Grignon (France, N 48.84°, E 1.95°). A wide range of measurements were made, for nutrient cycles and organic matter in particular, for an analysis of interactions occurring during the emissions of greenhouse gases. We focus here on nitrogen (N) data collected over eight years (2009-2016). The data include: nitrous oxide fluxes (N2O), soil N contents (NO3- and NH4+), aboveground plant N content and biomass at maturity, yield, agricultural practices including N spreading, and climate. The four systems differ in terms of tillage practices, N inputs, and species, which is likely to affect soil N. Field data were collected and N2O fluxes were calculated. These original new cropping systems are innovating, resulting in new combinations of agricultural practices. The data obtained could be used to improve models for parameterization and validation, and to increase the predictive accuracy of models of N losses in original conditions.

With the development of agroecosystem approaches, new cropping systems have to be designed to deliver multiple ecosystem services. In this context, we assessed four innovative cropping systems, designed to reach multiple environmental and production goals, in a long-term field experiment (2009–2020) at Grignon (France, N 48.84°, E 1.95°). A wide range of measurements were made, for nutrient cycles and organic matter in particular, for an analysis of interactions occurring during the emissions of greenhouse gases. We focus here on nitrogen (N) data collected over eight years (2009–2016). The data include: nitrous oxide fluxes (N₂O), soil N contents (NO₃⁻ and NH₄⁺), aboveground plant N content and biomass at maturity, yield, agricultural practices including N spreading, and climate. The four systems differ in terms of tillage practices, N inputs, and species, which is likely to affect soil N. Field data were collected and N₂O fluxes were calculated. These original new cropping systems are innovating, resulting in new combinations of agricultural practices. The data obtained could be used to improve models for parameterization and validation, and to increase the predictive accuracy of models of N losses in original conditions.