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Enregistrement W2998439172 · doi:10.2760/638382

Adoption of cover crops for climate change mitigation in the EU

2019· preprint· en· W2998439172 sur OpenAlexaff
A.B. Smit, S.R.M. Janssens, W.K. van Leeuwen-Haagsma, W.H.G.J. Hennen, José Luis Andrados, Jonas Kathage, Ignácio Pérez Domínguez

Notice bibliographique

RevueJoint Research Centre (European Commission) · 2019
Typepreprint
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueSoil Carbon and Nitrogen Dynamics
Établissements canadiensImpact
Organismes subventionnairesnon disponible
Mots-clésCover cropEnvironmental scienceGreenhouse gasAgronomySeedbedAgricultureSoil carbonCarbon sequestrationAgroforestryCropGeographySowingBiologySoil waterCarbon dioxide

Résumé

récupéré en direct d'OpenAlex

In order to contribute to the EU's ambitions to reduce its greenhouse gas emissions by 2030, different technological and management options are being analysed. Within the agricultural sector, catch and cover crops (CCC) are considered a viable option to mitigate greenhouse gas emissions. CCC are crops grown for the protection of the agricultural land which would otherwise be bare against erosion and nutrient losses. They immobilise nitrogen such that it remains available in the soil after the harvest of the main crop for the next main crop. If managed correctly, catch and cover crops can enhance climate change mitigation through soil carbon sequestration (building up the soil organic carbon content of the soil) and reducing emissions from fertiliser production.\nIn this report, we conduct a survey for different case study regions in Europe (Castilla y León in Spain; Sud – Muntenia in Romania; Centre in France; and Overijssel in the Netherlands) focusing on the mitigation and adoption potential. From the survey results we observe that CCC are mainly grown after wheat, barley, silage maize or sunflower, the most popular species being ryegrasses, mustards, clovers, vetch, oats, phacelia and rye. In most cases CCC are sown after the harvest of the main crop, after a seedbed preparation, and adopters generally do not apply irrigation, N-fertilisation (mineral or organic) or crop protection. The termination of these crops is in most cases by ploughing or by using herbicides (glyphosate). \nIn Spain, the concept of CCC is not very well known. Common vetch was the most applied species, mostly after cereals but in some cases after sugar beet or potato. Part of the CCC was undersown. Irrigation and N-manure were often applied, but seedbed preparation, N-fertiliser and crop protection were not frequently mentioned. Half of the Spanish adopters did not harvest this crop and the other half harvested it for selling, for own use or for fodder. The majority of adopters used ploughing for termination of CCC.\nIn France, unlike the other regions in the survey, a wide variety of CCC species was applied. Black oat (Avena strigosa), white mustard (Sinapis alba), common vetch (Vicia sativa) and Phacelia were most frequently mentioned, which were mostly sown after wheat and barley harvest. French farmers are in general well informed about catch and cover crops. While most farmers apply seedbed preparation, irrigation, N-fertilization and crop protection are not often applied. The large majority of French adopters did not harvest the CCC and terminated the crop through ploughing.\nDutch respondents knew the CCC-concept, since most of them grew green maize on sandy soils as a part of their fodder production for their dairy herd. Thus, they had to comply with the Nitrates Directive to grow a CCC after the maize and they did that mostly after harvest. Half of them grew Italian or English ryegrass and the other half (cutting) rye. This practice led to a relatively long CCC-period on the field compared to the three other regions in the survey. Irrigation, N-fertilisation, N-manure and crop protection were not often applied, but all adopters applied seedbed preparation. Half of the CCC-growers terminated the crop through ploughing, a quarter through a different mechanical form and the others through herbicides. \nIn Romania, not all farmers knew the concept of CCC-growing, although quite a share of the adopters did so as an obligation by the Romanian Agency for Payments and Intervention for Agriculture. Rapeseed and green peas were the most frequently applied CCC-species, after wheat or sunflower harvest and after a seedbed preparation. Like in the other regions, irrigation, N-fertilisation, N-manure and crop protection were not often applied. The majority of adopters did not harvest the crop and more than 80% of the adopters in Romania ploughed the CCC for termination.\nFarming activities related to the use of CCC take on average 3.4 hours per ha. The total cost of all inputs (seeds, fertiliser/manure, pesticides, water) and all operations (seedbed preparation, sowing, application of fertiliser/manure and plant protection, irrigation, fuel, harvest and termination, including contractors hired) is on average 144 €/ha. Adopters estimated that growing CCC reduces the fertiliser need of the following main crops by 6.6%, and increases yields of the following main crops by 4.2%.\nMost adopters grow CCC because of existing policies and most consider cultivation mandatory. Overall, agronomic reasons play a smaller role, and environmental motives are of little relevance to the adoption decision. The reasons why non-adopters do not grow CCC include a lack of benefits, high cost and labour requirements, lack of awareness, and unsuitable weather and crop rotations, among others. A majority of non-adopters indicate that they would start growing CCC if additional subsidies were provided.\nEstimated CCC adoption rates based on the share of farmers using CCC range from 12% in Castilla y León, 46% in Sud – Muntenia, 84% in Centre to 99% in Overijssel. However, most adopters grow CCC on only a small share of their arable land, with the exception of Overijssel. The estimated adoption rate based on the regional area potentially available for CCC cultivation (after cereals, protein and industrial crops) is well below 20% in the Spanish, Romanian and French regions and 90% in Overijssel. The adoption potential is combined with regionally differentiated estimates of carbon sequestration from CCC per hectare to calculate the total potential climate change mitigation from CCC in each of the case study regions.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,737
Score d'incertitude au seuil0,351

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,121
Tête enseignante GPT0,320
Écart entre enseignants0,200 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations19
Publié2019
Routes d'admission1
Résumé présentoui

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