Climate‐smart agriculture and forestry: maintaining plant productivity in a changing world while minimizing production system effects on climate
Notice bibliographique
Résumé
Global climatic and atmospheric change represents a major threat to the productive capacity of our crop and forest production systems. Rising atmospheric CO2 concentrations cause warming and altered precipitation regimes, which will lead to more extreme weather events, including heat waves and droughts (International Panel on Climate Change 2014). At the same time, increasing surface ozone concentrations directly impact plant production, decreasing yield. Changes in climate have already impacted yields in key crop species, lowering them in some regions (Ray et al. 2012; International Panel on Climate Change 2014), and will continue to impact our crop and forest production systems in the coming decades, a period when we not only need to maintain current levels of food and wood production, but also increase productivity to feed and supply the world's growing population. To meet these needs, we must adapt our practices to identify and create heat-, drought- and ozone-tolerant varieties for use in agriculture and forestry, while developing cultivars that are more responsive to the increases that have and will occur in atmospheric [CO2]. A key priority will also be to select the cultivars that will be most productive in the warmer, drier, high CO2 climate of the future and to search for ways to maximize plant productivity on marginal lands that are currently too saline or dry for the production of food, woody biomass and other bioproducts. But while agriculture and forestry are affected by climate change, these activities are also a significant cause of the changes to the climate system. Climate change is driven by net greenhouse gas emissions to the atmosphere, of which agriculture accounts for about 15% and land-use change (largely deforestation) another 15% (Ciais et al. 2013). As such, agricultural and forestry practices also provide a unique opportunity to mitigate climate change. By carefully selecting which species or genotype we use in farmers' fields and forestry plantations, we can increase the water-use efficiency of plant production, impact the regional energy balance through shifts in canopy albedo, improve carbon sequestration and minimize volatile organic compound (VOC) emissions that can exacerbate climate change. This Special Issue therefore addresses the potential to adapt to and mitigate climate change via plant selection and modification at multiple scales in managed production systems, an approach known as climate-smart agriculture and forestry (Food and Agriculture Organization of the United Nations 2013; Lipper et al. 2015). To understand how crop and forest systems must adapt to climate change, we first need a firm understanding of how rising temperatures and drought stress impact plant function and productivity. Rice provides more dietary calories to humans than any other source. Jagadish et al. (2015) review the effects of heat stress on rice, particularly under water-conserving growth conditions, outlining the main challenges for improving the heat tolerance of this globally critical crop species. Teskey et al. (2015) address heat stress, but concentrate on woody species, discussing how trees cope with not only warmer conditions, but specifically with the extreme heat events that are expected to become more common in the coming decades. In many forest systems, the combination of heat and drought stress is considered the cause of decreased forest productivity and increased mortality. Zwieniecki & Secchi (2015) show how the hydraulic function of trees will, without adaptation, be impaired by future climate conditions. Although transplantation of production systems poleward to maintain a similar climate envelope for growth is considered an adaptation to climate change, Way & Montgomery (2015) discuss why photoperiod effects may limit the effectiveness of this strategy in trees. Sphagnum-dominated peat ecosystems include some of the largest soil organic carbon stores on the planet. Understanding how these respond to climate change, as reviewed by Weston et al. (2015), is critical to being able to develop future management strategies. Another set of papers in this issue focus on using existing variation in plants to adapt agriculture and forestry to climate change. Aspinwall et al. (2015) discuss how variation in phenotypic plasticity to climate change drivers within a species can be used to promote crop and tree productivity in a changing environment. Soybean is the world's single most important protein source. Bishop et al. (2015) identify the genetic variability in the responsiveness of soybean to rising [CO2], showing the prospect for the breeding of improved cultivars. Other traits that can be explored to maximize productivity and resource-use efficiency include root morphology and aquaporin regulation. Lynch (2015) reviews the potential for selecting plants with more efficient root phenes to increase crop yield and plant water and nutrient acquisition, while Moshelion et al. (2015) highlight the role of aquaporins in mediating CO2 and water fluxes and crop water-use efficiency. Expanding forestry production onto marginal lands will require drought and salt tolerant trees. Polle & Chen (2015) discuss how trees that successfully grow in saline environments cope with salinity stress from the molecular to the whole plant scale. While we can take advantage of the current variation in our crop and forestry species, we can also engineer novel trait combinations or improved stress tolerance into species of interest to adapt our production systems to future climates and mitigate the effects of agriculture and forestry on climate. Carmo-Silva et al. (2015) outline how we can modify Rubisco (the key carboxylating enzyme in the Calvin-Benson cycle) and its regulatory processes to increase photosynthetic CO2 fixation rates and yield in crops. Borland et al. (2015) take this idea further, describing how engineering Crassulacean acid metabolism (CAM) photosynthesis into trees could increase the water-use efficiency and productivity of agroforestry plantations. Wang et al. (2015) show a new framework for predicting the spatial and temporal variation in yields of emerging energy crops from mechanism, including responses to global climatic and atmospheric change. Nemali et al. (2015) investigate the physiology underlying how a maize line that has been bioengineered with a bacterial cold shock protein increases drought tolerance and yield under water deficit. And Fragkostefanakis et al. (2015) discuss how we can manipulate heat-shock transcription factors and heat-shock proteins to increase the tolerance of crop species to the higher temperatures and greater frequency of heat waves predicted for the future. The last set of papers in this issue examine how the choices we make in adapting agriculture and forestry for climate change can have impact at regional and larger scales. Rosenkranz et al. (2015) discuss how to manage VOC emissions from our production systems by choosing appropriate genotypes and modifying land management strategies. Bagley et al. (2015) model the regional biophysical impacts of adopting various climate-smart agricultural approaches, including expansion of no-till agriculture and adoption of perennial crops in the American Midwest. Devaraju et al. (2015) highlight the importance of considering both changes in CO2 fluxes and in biophysical properties of vegetation when considering the impact of climate-smart schemes on regional and global climate. Lastly, Thorley et al. (2015) explore how tree roots and their mycorrhizal associates can affect rock weathering and thereby provide one means to mitigate climate change over the next century. Taken together, the papers in this issue show multiple ways in which, with modest research and development investment, land management could continue to provide food, feed and bioproducts for a growing human population through adaptation of our production systems to global atmospheric and climatic change, while simultaneously providing a means to mitigate further change in climate.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».