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Enregistrement W4247951249 · doi:10.2134/csa2017.62.0711

Canola as a Winter Crop in California

2017· article· en· W4247951249 sur OpenAlexaboutno aff
Tracy Hmielowski

Notice bibliographique

RevueCSA News · 2017
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueNitrogen and Sulfur Effects on Brassica
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCanolaBrassicaAgronomyCropAgricultureBiologyEcology

Résumé

récupéré en direct d'OpenAlex

Variation in flowering and maturity among canola varieties. Photo courtesy of S. Kaffka. Adapted and un-adapted canola varieties. Photo courtesy of N. George. Agriculture is a major part of the economy in California. Farmers grow a wide variety of crops, including almonds, oranges, and tomatoes, but these are primarily perennial or warm-season crops. In the winter, or cool-season, the Mediterranean climate of California could also support brassica species, including oilseeds like canola (Brassica napus L.). Currently, safflower (Carthamus tinctorius) is the main oilseed grown in California, but cultivars of canola adapted to these climate conditions may provide a new option for growers. Stephen Kaffka and Nicholas George at the University of California–Davis have evaluated oilseeds as cool-season crops in California. “I was familiar with [canola] from back in Australia,” explains ASA and CSSA member George, who is originally from Australia. “And you could [see the] potential for it as a crop here.” Recent changes in California energy policy and restrictions on irrigation water supplies have also increased the interest in oilseeds as winter crops. “[The new policies] desire to see in-state feedstock production for in-state fuel production,” says Kaffka, a member of ASA, CSSA, and SSSA. “So, besides the food oil and protein feed markets available for canola, there would be also an industrial use as a feedstock for bio-diesel or potentially bio-jet fuels.” Given the potential for canola, the researchers wanted to model canola production across the state. Modeling is a faster and less expensive approach for evaluating how canola might perform at different locations compared with field studies. Kaffka and George report their findings in Agronomy Journal (http://bit.ly/2sZD47B). The first step was to evaluate the reliability of the Agricultural Production Systems Simulator, or APSIM, model for simulating canola production in California. “APSIM uses input information regarding things such as climate, crop management, and soil to simulate various biophysical aspects of cropping systems and their interactions, such as the phenology, water, and nutrient uptake of many crop species, and the behavior of water, solutes and organic matter in the soil,” George explains. To evaluate the APSIM model, the authors used data from a multi-site study, where different varieties of canola were grown across California. Details of the field study were published in Crop Science (http://bit.ly/2rY7zMQ). The authors found that the predicted values for seed yield, biomass accumulation, seed production, and phenology from the APSIM model had good agreement with observed values from the field study. Satisfied with the accuracy of the APSIM model, the authors were able to run numerous scenarios. Specifically, the authors evaluated canola growth under different irrigation scenarios and potential future climate conditions. Irrigation simulations included rainfed, pre-sowing irrigation, and irrigation throughout the winter. Precipitation was expected to be a limiting factor, and only one region, the northern Central Valley, is likely to support rainfed production. The estimated yield from rainfed production in the region was 3,500 kg/ha. Other regions modeled, including the San Joaquin Valley and Imperial Valley, would require irrigation for production to be economically viable. The predicted yields for canola crops with irrigation ranged from 4,000 to 6,000 kg/ha, depending upon site and irrigation practice. The researchers also evaluated canola production under future climate conditions, which are predicted to become warmer and dryer. Although any future climate predictions come with uncertainty, “the modeling at least provides a baseline for thinking that [canola] will remain a useful crop choice for the foreseeable future under changing climate,” Kaffka says. In a low CO2 emissions simulation, yield losses were as great as 20%. However, under a high-emission scenario, yields did not decline. This suggests that greater CO2 levels could offset some of the negative impact of increased temperature. In addition to canola's value for food and biofuel production, farmers could benefit from added crop choices in the winter period. For example, canola can act as a disease break in wheat fields and a resource for pollinators. “Having good pollen and nectar sources like canola throughout the state would be very beneficial for beekeepers and native pollinator species,” Kaffka says. Considering the reliance upon bees for pollination of crops like almonds, canola can have an indirect benefit on other crops. There are some challenges to widespread adoption of canola as a winter crop. For one, “[farmers] have to really be a bit more timely in [their] management of canola compared with wheat,” Kaffka says, given the narrower harvest window for canola. There is also an issue of perception—if farmers perceive canola to be difficult to grow, or if it is not profitable, they will not grow the crop. To overcome hesitation on the part of farmers will require additional research and economic analyses. The high yields observed in trials of the most promising varieties are essential. Kaffka and George hope to continue to collaborate with and learn from canola growers and researchers across the U.S., Canada, and Australia. Dig Deeper Read the open access Agronomy Journal article, “Canola as a New Crop for California: A Simulation Study,” at http://bit.ly/2sZD47B and the open access Crop Science article, “Canola and Camelina as New Crop Options for Cool-Season Production in California,” at http://bit.ly/2rY7zMQ.

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)0,000
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: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,394
Score d'incertitude au seuil0,361

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
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 ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,009
Tête enseignante GPT0,272
Écart entre enseignants0,263 · 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'étudeExpérimental (laboratoire)
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

Citations1
Publié2017
Routes d'admission1
Résumé présentoui

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